Overview

Dataset statistics

Number of variables128
Number of observations301823
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory294.7 MiB
Average record size in memory1.0 KiB

Variable types

Numeric15
Categorical94
Text19

Alerts

NPCHP1 is highly imbalanced (82.5%)Imbalance
NPCHP3 is highly imbalanced (64.0%)Imbalance
NPCHP5 is highly imbalanced (59.3%)Imbalance
NPCHP6 is highly imbalanced (55.7%)Imbalance
NPCHP6A is highly imbalanced (54.5%)Imbalance
NPCHP7 is highly imbalanced (84.1%)Imbalance
NPCHP9A is highly imbalanced (65.4%)Imbalance
NPCHP9B is highly imbalanced (65.6%)Imbalance
NPCHP9C is highly imbalanced (52.5%)Imbalance
NPCHP9D is highly imbalanced (77.9%)Imbalance
NPCHP9E is highly imbalanced (77.7%)Imbalance
NPCHP9F is highly imbalanced (56.0%)Imbalance
NPCHP12A is highly imbalanced (67.0%)Imbalance
NPCHP13 is highly imbalanced (53.4%)Imbalance
NPCHP13A is highly imbalanced (98.3%)Imbalance
NPCHP14 is highly imbalanced (77.7%)Imbalance
NPCHP16 is highly imbalanced (74.8%)Imbalance
NPCHP17 is highly imbalanced (63.5%)Imbalance
NPCHP18A is highly imbalanced (73.9%)Imbalance
NPCHP18B is highly imbalanced (83.7%)Imbalance
NPCHP18C is highly imbalanced (80.8%)Imbalance
NPCHP18D is highly imbalanced (89.9%)Imbalance
NPCHP18E is highly imbalanced (95.9%)Imbalance
NPCHP18F is highly imbalanced (93.9%)Imbalance
NPCHP18G is highly imbalanced (91.8%)Imbalance
NPCHP18H is highly imbalanced (79.2%)Imbalance
NPCHP18J is highly imbalanced (93.6%)Imbalance
NPCHP18K is highly imbalanced (99.5%)Imbalance
NPCHP18L is highly imbalanced (99.9%)Imbalance
NPCHP18M is highly imbalanced (96.4%)Imbalance
NPCHP24_1A is highly imbalanced (95.8%)Imbalance
NPCHP24AB is highly imbalanced (97.9%)Imbalance
NPCHP24_1B is highly imbalanced (98.0%)Imbalance
NPCHP24BB is highly imbalanced (99.0%)Imbalance
NPCHP25A is highly imbalanced (97.5%)Imbalance
NPCHP25B is highly imbalanced (98.6%)Imbalance
NPCHP25C is highly imbalanced (99.7%)Imbalance
NPCHP25D is highly imbalanced (99.2%)Imbalance
NPCHP25E is highly imbalanced (99.5%)Imbalance
NPCHP25F is highly imbalanced (99.7%)Imbalance
NPCHP25G is highly imbalanced (99.5%)Imbalance
NPCHP25H is highly imbalanced (99.2%)Imbalance
NPCHP25I is highly imbalanced (99.5%)Imbalance
NPCHP28B is highly imbalanced (96.8%)Imbalance
NPCHP29A is highly imbalanced (98.6%)Imbalance
NPCHP29B is highly imbalanced (96.7%)Imbalance
NPCHP29C is highly imbalanced (97.9%)Imbalance
NPCHP29D is highly imbalanced (99.8%)Imbalance
NPCHP29E is highly imbalanced (99.9%)Imbalance
NPCHP29F is highly imbalanced (99.9%)Imbalance
NPCHP29G is highly imbalanced (99.6%)Imbalance
NPCHP29H is highly imbalanced (99.9%)Imbalance
NPCHP29I is highly imbalanced (99.8%)Imbalance
NPCHP30C is highly imbalanced (61.2%)Imbalance
NPCHP30D is highly imbalanced (75.2%)Imbalance
NPCHP30E is highly imbalanced (68.5%)Imbalance
NPCHP31DB is highly imbalanced (76.0%)Imbalance
NPCHP32 is highly imbalanced (61.8%)Imbalance
NPCHP32A is highly imbalanced (77.9%)Imbalance
NPCHP32B is highly imbalanced (91.8%)Imbalance
NPCHP34 is highly imbalanced (95.3%)Imbalance
NPCHP34A is highly imbalanced (98.8%)Imbalance
ĆÆĀ»ĀæDIRECTORIO_PER has unique valuesUnique
NPCHP31AA has 61675 (20.4%) zerosZeros
NPCHP31AB has 38118 (12.6%) zerosZeros
NPCHP31BA has 157243 (52.1%) zerosZeros
NPCHP31BB has 165070 (54.7%) zerosZeros
NPCHP31CA has 3244 (1.1%) zerosZeros
NPCHP31CB has 3546 (1.2%) zerosZeros
NPCHP31EA has 188101 (62.3%) zerosZeros
NPCHP31EB has 168356 (55.8%) zerosZeros
NPCHP31FA has 159691 (52.9%) zerosZeros
NPCHP31FB has 198734 (65.8%) zerosZeros

Reproduction

Analysis started2024-05-07 04:45:48.299738
Analysis finished2024-05-07 04:47:12.648785
Duration1 minute and 24.35 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

ĆÆĀ»ĀæDIRECTORIO_PER
Real number (ℝ)

UNIQUE 

Distinct301823
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19654799
Minimum10100011
Maximum3.1754311 Ɨ 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:12.765771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10100011
5-th percentile10911814
Q114569212
median18196411
Q324858012
95-th percentile29414512
Maximum3.1754311 Ɨ 108
Range3.074431 Ɨ 108
Interquartile range (IQR)10288801

Descriptive statistics

Standard deviation7114051.2
Coefficient of variation (CV)0.36194983
Kurtosis258.34251
Mean19654799
Median Absolute Deviation (MAD)4775799
Skewness8.8386188
Sum5.9322704 Ɨ 1012
Variance5.0609725 Ɨ 1013
MonotonicityNot monotonic
2024-05-06T23:47:12.910103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100011 1
 
< 0.1%
23214113 1
 
< 0.1%
23214813 1
 
< 0.1%
23214812 1
 
< 0.1%
23214811 1
 
< 0.1%
23214711 1
 
< 0.1%
23214611 1
 
< 0.1%
23214513 1
 
< 0.1%
23214512 1
 
< 0.1%
23214511 1
 
< 0.1%
Other values (301813) 301813
> 99.9%
ValueCountFrequency (%)
10100011 1
< 0.1%
10100012 1
< 0.1%
10100013 1
< 0.1%
10100111 1
< 0.1%
10100112 1
< 0.1%
10100113 1
< 0.1%
10100211 1
< 0.1%
10100212 1
< 0.1%
10100311 1
< 0.1%
10100312 1
< 0.1%
ValueCountFrequency (%)
317543112 1
< 0.1%
317543111 1
< 0.1%
317543110 1
< 0.1%
317463110 1
< 0.1%
315230110 1
< 0.1%
281937110 1
< 0.1%
281084110 1
< 0.1%
280308112 1
< 0.1%
277416110 1
< 0.1%
275162111 1
< 0.1%

DIRECTORIO_HOG
Real number (ℝ)

Distinct109111
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1956211.7
Minimum1010001
Maximum3178851
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:13.053803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1010001
5-th percentile1091152
Q11456761
median1819161
Q32485181
95-th percentile2940711
Maximum3178851
Range2168850
Interquartile range (IQR)1028420

Descriptive statistics

Standard deviation587397.84
Coefficient of variation (CV)0.30027315
Kurtosis-1.156378
Mean1956211.7
Median Absolute Deviation (MAD)477460
Skewness0.2574394
Sum5.9042967 Ɨ 1011
Variance3.4503622 Ɨ 1011
MonotonicityIncreasing
2024-05-06T23:47:13.198622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409051 15
 
< 0.1%
1091071 14
 
< 0.1%
1902271 13
 
< 0.1%
2582891 13
 
< 0.1%
1473411 13
 
< 0.1%
1697181 12
 
< 0.1%
1692321 12
 
< 0.1%
1609311 12
 
< 0.1%
1294831 12
 
< 0.1%
1590861 12
 
< 0.1%
Other values (109101) 301695
> 99.9%
ValueCountFrequency (%)
1010001 3
< 0.1%
1010011 3
< 0.1%
1010021 2
< 0.1%
1010031 3
< 0.1%
1010041 1
 
< 0.1%
1010051 1
 
< 0.1%
1010061 1
 
< 0.1%
1010071 4
< 0.1%
1010081 4
< 0.1%
1010082 3
< 0.1%
ValueCountFrequency (%)
3178851 2
< 0.1%
3178811 2
< 0.1%
3178741 1
 
< 0.1%
3178591 1
 
< 0.1%
3178441 2
< 0.1%
3178351 4
< 0.1%
3178341 2
< 0.1%
3178321 2
< 0.1%
3178311 2
< 0.1%
3178251 4
< 0.1%

DIRECTORIO
Real number (ℝ)

Distinct107218
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195621.06
Minimum101000
Maximum317885
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:13.335417image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile109115.1
Q1145676
median181916
Q3248518
95-th percentile294071
Maximum317885
Range216885
Interquartile range (IQR)102842

Descriptive statistics

Standard deviation58739.784
Coefficient of variation (CV)0.30027331
Kurtosis-1.156378
Mean195621.06
Median Absolute Deviation (MAD)47746
Skewness0.25743939
Sum5.9042936 Ɨ 1010
Variance3.4503623 Ɨ 109
MonotonicityIncreasing
2024-05-06T23:47:13.467419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145788 21
 
< 0.1%
112379 19
 
< 0.1%
184980 19
 
< 0.1%
135993 18
 
< 0.1%
172991 18
 
< 0.1%
145803 17
 
< 0.1%
111041 16
 
< 0.1%
139511 16
 
< 0.1%
136299 15
 
< 0.1%
145884 15
 
< 0.1%
Other values (107208) 301649
99.9%
ValueCountFrequency (%)
101000 3
< 0.1%
101001 3
< 0.1%
101002 2
 
< 0.1%
101003 3
< 0.1%
101004 1
 
< 0.1%
101005 1
 
< 0.1%
101006 1
 
< 0.1%
101007 4
< 0.1%
101008 7
< 0.1%
101009 4
< 0.1%
ValueCountFrequency (%)
317885 2
< 0.1%
317881 2
< 0.1%
317874 1
 
< 0.1%
317859 1
 
< 0.1%
317844 2
< 0.1%
317835 4
< 0.1%
317834 2
< 0.1%
317832 2
< 0.1%
317831 2
< 0.1%
317825 4
< 0.1%

SECUENCIA_P
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0188587
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:13.581846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17328154
Coefficient of variation (CV)0.17007416
Kurtosis210.06825
Mean1.0188587
Median Absolute Deviation (MAD)0
Skewness12.355759
Sum307515
Variance0.030026493
MonotonicityNot monotonic
2024-05-06T23:47:13.696735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 297424
98.5%
2 3429
 
1.1%
3 729
 
0.2%
4 184
 
0.1%
5 43
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
1 297424
98.5%
2 3429
 
1.1%
3 729
 
0.2%
4 184
 
0.1%
5 43
 
< 0.1%
6 7
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
< 0.1%
6 7
 
< 0.1%
5 43
 
< 0.1%
4 184
 
0.1%
3 729
 
0.2%
2 3429
 
1.1%
1 297424
98.5%

ORDEN
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2308969
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:13.801269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum16
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3038677
Coefficient of variation (CV)0.58445895
Kurtosis2.6573557
Mean2.2308969
Median Absolute Deviation (MAD)1
Skewness1.324084
Sum673336
Variance1.7000709
MonotonicityNot monotonic
2024-05-06T23:47:13.924376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 109111
36.2%
2 89174
29.5%
3 56191
18.6%
4 29787
 
9.9%
5 11311
 
3.7%
6 3852
 
1.3%
7 1423
 
0.5%
8 555
 
0.2%
9 244
 
0.1%
10 97
 
< 0.1%
Other values (6) 78
 
< 0.1%
ValueCountFrequency (%)
1 109111
36.2%
2 89174
29.5%
3 56191
18.6%
4 29787
 
9.9%
5 11311
 
3.7%
6 3852
 
1.3%
7 1423
 
0.5%
8 555
 
0.2%
9 244
 
0.1%
10 97
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
15 1
 
< 0.1%
14 4
 
< 0.1%
13 10
 
< 0.1%
12 23
 
< 0.1%
11 39
 
< 0.1%
10 97
 
< 0.1%
9 244
 
0.1%
8 555
 
0.2%
7 1423
0.5%

NPCHP1
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
1
293920 
2
 
7903

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

Length

2024-05-06T23:47:14.047071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:14.145106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

Most occurring characters

ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 301823
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 293920
97.4%
2 7903
 
2.6%

NPCHP2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
221089 
1
80734 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

Length

2024-05-06T23:47:14.238078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:14.327372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

Most occurring characters

ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 301823
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 221089
73.3%
1 80734
 
26.7%

NPCHP3
Categorical

IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
233944 
6
 
19785
3
 
18112
2
 
13970
5
 
3654
Other values (9)
 
12358

Length

Max length2
Median length1
Mean length1.014038
Min length1

Characters and Unicode

Total characters306060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row2
4th row
5th row

Common Values

ValueCountFrequency (%)
233944
77.5%
6 19785
 
6.6%
3 18112
 
6.0%
2 13970
 
4.6%
5 3654
 
1.2%
1 2651
 
0.9%
8 2545
 
0.8%
14 2383
 
0.8%
7 2335
 
0.8%
12 666
 
0.2%
Other values (4) 1778
 
0.6%

Length

2024-05-06T23:47:14.429207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 19785
29.1%
3 18112
26.7%
2 13970
20.6%
5 3654
 
5.4%
1 2651
 
3.9%
8 2545
 
3.7%
14 2383
 
3.5%
7 2335
 
3.4%
12 666
 
1.0%
9 590
 
0.9%
Other values (3) 1188
 
1.8%

Most occurring characters

ValueCountFrequency (%)
233944
76.4%
6 19785
 
6.5%
3 18687
 
6.1%
2 14636
 
4.8%
1 7319
 
2.4%
5 3654
 
1.2%
8 2545
 
0.8%
4 2383
 
0.8%
7 2335
 
0.8%
9 590
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 233944
76.4%
Decimal Number 72116
 
23.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 19785
27.4%
3 18687
25.9%
2 14636
20.3%
1 7319
 
10.1%
5 3654
 
5.1%
8 2545
 
3.5%
4 2383
 
3.3%
7 2335
 
3.2%
9 590
 
0.8%
0 182
 
0.3%
Space Separator
ValueCountFrequency (%)
233944
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 306060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
233944
76.4%
6 19785
 
6.5%
3 18687
 
6.1%
2 14636
 
4.8%
1 7319
 
2.4%
5 3654
 
1.2%
8 2545
 
0.8%
4 2383
 
0.8%
7 2335
 
0.8%
9 590
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
233944
76.4%
6 19785
 
6.5%
3 18687
 
6.1%
2 14636
 
4.8%
1 7319
 
2.4%
5 3654
 
1.2%
8 2545
 
0.8%
4 2383
 
0.8%
7 2335
 
0.8%
9 590
 
0.2%

NPCHP4
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
80734 
5
63280 
3
45018 
9
32509 
4
25011 
Other values (11)
55271 

Length

Max length2
Median length1
Mean length1.0464643
Min length1

Characters and Unicode

Total characters315847
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row11
3rd row8
4th row
5th row7

Common Values

ValueCountFrequency (%)
80734
26.7%
5 63280
21.0%
3 45018
14.9%
9 32509
10.8%
4 25011
 
8.3%
6 21171
 
7.0%
11 9171
 
3.0%
7 8823
 
2.9%
1 5574
 
1.8%
8 5152
 
1.7%
Other values (6) 5380
 
1.8%

Length

2024-05-06T23:47:14.537318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5 63280
28.6%
3 45018
20.4%
9 32509
14.7%
4 25011
 
11.3%
6 21171
 
9.6%
11 9171
 
4.1%
7 8823
 
4.0%
1 5574
 
2.5%
8 5152
 
2.3%
13 4031
 
1.8%
Other values (5) 1349
 
0.6%

Most occurring characters

ValueCountFrequency (%)
80734
25.6%
5 63442
20.1%
3 49049
15.5%
9 32509
10.3%
1 28769
 
9.1%
4 25016
 
7.9%
6 21171
 
6.7%
7 8823
 
2.8%
8 5152
 
1.6%
2 806
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 235113
74.4%
Space Separator 80734
 
25.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 63442
27.0%
3 49049
20.9%
9 32509
13.8%
1 28769
12.2%
4 25016
 
10.6%
6 21171
 
9.0%
7 8823
 
3.8%
8 5152
 
2.2%
2 806
 
0.3%
0 376
 
0.2%
Space Separator
ValueCountFrequency (%)
80734
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 315847
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
80734
25.6%
5 63442
20.1%
3 49049
15.5%
9 32509
10.3%
1 28769
 
9.1%
4 25016
 
7.9%
6 21171
 
6.7%
7 8823
 
2.8%
8 5152
 
1.6%
2 806
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 315847
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80734
25.6%
5 63442
20.1%
3 49049
15.5%
9 32509
10.3%
1 28769
 
9.1%
4 25016
 
7.9%
6 21171
 
6.7%
7 8823
 
2.8%
8 5152
 
1.6%
2 806
 
0.3%

NPCHP4A
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
86308 
11
58313 
5
50762 
2
24533 
3
21225 
Other values (9)
60682 

Length

Max length2
Median length1
Mean length1.2096593
Min length1

Characters and Unicode

Total characters365103
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row1
3rd row5
4th row
5th row4

Common Values

ValueCountFrequency (%)
86308
28.6%
11 58313
19.3%
5 50762
16.8%
2 24533
 
8.1%
3 21225
 
7.0%
7 11252
 
3.7%
4 11244
 
3.7%
1 10755
 
3.6%
9 8868
 
2.9%
6 7705
 
2.6%
Other values (4) 10858
 
3.6%

Length

2024-05-06T23:47:14.645558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11 58313
27.1%
5 50762
23.6%
2 24533
11.4%
3 21225
 
9.8%
7 11252
 
5.2%
4 11244
 
5.2%
1 10755
 
5.0%
9 8868
 
4.1%
6 7705
 
3.6%
8 5891
 
2.7%
Other values (3) 4967
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 132348
36.2%
86308
23.6%
5 50762
 
13.9%
2 25391
 
7.0%
3 22400
 
6.1%
7 11252
 
3.1%
4 11244
 
3.1%
9 8868
 
2.4%
6 7705
 
2.1%
8 5891
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 278795
76.4%
Space Separator 86308
 
23.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 132348
47.5%
5 50762
 
18.2%
2 25391
 
9.1%
3 22400
 
8.0%
7 11252
 
4.0%
4 11244
 
4.0%
9 8868
 
3.2%
6 7705
 
2.8%
8 5891
 
2.1%
0 2934
 
1.1%
Space Separator
ValueCountFrequency (%)
86308
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 365103
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 132348
36.2%
86308
23.6%
5 50762
 
13.9%
2 25391
 
7.0%
3 22400
 
6.1%
7 11252
 
3.1%
4 11244
 
3.1%
9 8868
 
2.4%
6 7705
 
2.1%
8 5891
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365103
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 132348
36.2%
86308
23.6%
5 50762
 
13.9%
2 25391
 
7.0%
3 22400
 
6.1%
7 11252
 
3.1%
4 11244
 
3.1%
9 8868
 
2.4%
6 7705
 
2.1%
8 5891
 
1.6%

NPCHP5
Categorical

IMBALANCE 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
220144 
5
25415 
3
 
13031
2
 
12215
7
 
8501
Other values (11)
22517 

Length

Max length2
Median length1
Mean length1.0065866
Min length1

Characters and Unicode

Total characters303811
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7
2nd row12
3rd row5
4th row
5th row4

Common Values

ValueCountFrequency (%)
220144
72.9%
5 25415
 
8.4%
3 13031
 
4.3%
2 12215
 
4.0%
7 8501
 
2.8%
6 7623
 
2.5%
4 5430
 
1.8%
1 3642
 
1.2%
8 2558
 
0.8%
9 1276
 
0.4%
Other values (6) 1988
 
0.7%

Length

2024-05-06T23:47:14.763627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5 25415
31.1%
3 13031
16.0%
2 12215
15.0%
7 8501
 
10.4%
6 7623
 
9.3%
4 5430
 
6.6%
1 3642
 
4.5%
8 2558
 
3.1%
9 1276
 
1.6%
10 1095
 
1.3%
Other values (5) 893
 
1.1%

Most occurring characters

ValueCountFrequency (%)
220144
72.5%
5 25545
 
8.4%
3 13107
 
4.3%
2 12524
 
4.1%
7 8501
 
2.8%
6 7623
 
2.5%
1 5924
 
1.9%
4 5514
 
1.8%
8 2558
 
0.8%
9 1276
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 220144
72.5%
Decimal Number 83667
 
27.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 25545
30.5%
3 13107
15.7%
2 12524
15.0%
7 8501
 
10.2%
6 7623
 
9.1%
1 5924
 
7.1%
4 5514
 
6.6%
8 2558
 
3.1%
9 1276
 
1.5%
0 1095
 
1.3%
Space Separator
ValueCountFrequency (%)
220144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 303811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
220144
72.5%
5 25545
 
8.4%
3 13107
 
4.3%
2 12524
 
4.1%
7 8501
 
2.8%
6 7623
 
2.5%
1 5924
 
1.9%
4 5514
 
1.8%
8 2558
 
0.8%
9 1276
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
220144
72.5%
5 25545
 
8.4%
3 13107
 
4.3%
2 12524
 
4.1%
7 8501
 
2.8%
6 7623
 
2.5%
1 5924
 
1.9%
4 5514
 
1.8%
8 2558
 
0.8%
9 1276
 
0.4%

NPCHP6
Categorical

IMBALANCE 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
22492 
3
 
19056
7
 
17066
4
 
9100
Other values (6)
 
13020

Length

Max length2
Median length1
Mean length1.000444
Min length1

Characters and Unicode

Total characters301957
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row9
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 22492
 
7.5%
3 19056
 
6.3%
7 17066
 
5.7%
4 9100
 
3.0%
1 5150
 
1.7%
5 3439
 
1.1%
6 2088
 
0.7%
8 1224
 
0.4%
9 985
 
0.3%

Length

2024-05-06T23:47:14.886806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 22492
27.9%
3 19056
23.6%
7 17066
21.1%
4 9100
11.3%
1 5150
 
6.4%
5 3439
 
4.3%
6 2088
 
2.6%
8 1224
 
1.5%
9 985
 
1.2%
10 134
 
0.2%

Most occurring characters

ValueCountFrequency (%)
221089
73.2%
2 22492
 
7.4%
3 19056
 
6.3%
7 17066
 
5.7%
4 9100
 
3.0%
1 5284
 
1.7%
5 3439
 
1.1%
6 2088
 
0.7%
8 1224
 
0.4%
9 985
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.2%
Decimal Number 80868
 
26.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22492
27.8%
3 19056
23.6%
7 17066
21.1%
4 9100
11.3%
1 5284
 
6.5%
5 3439
 
4.3%
6 2088
 
2.6%
8 1224
 
1.5%
9 985
 
1.2%
0 134
 
0.2%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301957
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.2%
2 22492
 
7.4%
3 19056
 
6.3%
7 17066
 
5.7%
4 9100
 
3.0%
1 5284
 
1.7%
5 3439
 
1.1%
6 2088
 
0.7%
8 1224
 
0.4%
9 985
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301957
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.2%
2 22492
 
7.4%
3 19056
 
6.3%
7 17066
 
5.7%
4 9100
 
3.0%
1 5284
 
1.7%
5 3439
 
1.1%
6 2088
 
0.7%
8 1224
 
0.4%
9 985
 
0.3%

NPCHP6A
Categorical

IMBALANCE 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
1
 
14123
2
 
11158
3
 
10216
4
 
8457
Other values (9)
36780 

Length

Max length2
Median length1
Mean length1.0301501
Min length1

Characters and Unicode

Total characters310923
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
1 14123
 
4.7%
2 11158
 
3.7%
3 10216
 
3.4%
4 8457
 
2.8%
5 7306
 
2.4%
7 5665
 
1.9%
6 5551
 
1.8%
11 4677
 
1.5%
9 4590
 
1.5%
Other values (4) 8991
 
3.0%

Length

2024-05-06T23:47:15.002012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 14123
17.5%
2 11158
13.8%
3 10216
12.7%
4 8457
10.5%
5 7306
9.0%
7 5665
7.0%
6 5551
 
6.9%
11 4677
 
5.8%
9 4590
 
5.7%
8 4568
 
5.7%
Other values (3) 4423
 
5.5%

Most occurring characters

ValueCountFrequency (%)
221089
71.1%
1 27900
 
9.0%
2 11220
 
3.6%
3 10274
 
3.3%
4 8457
 
2.7%
5 7306
 
2.3%
7 5665
 
1.8%
6 5551
 
1.8%
9 4590
 
1.5%
8 4568
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
71.1%
Decimal Number 89834
28.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 27900
31.1%
2 11220
12.5%
3 10274
 
11.4%
4 8457
 
9.4%
5 7306
 
8.1%
7 5665
 
6.3%
6 5551
 
6.2%
9 4590
 
5.1%
8 4568
 
5.1%
0 4303
 
4.8%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310923
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
71.1%
1 27900
 
9.0%
2 11220
 
3.6%
3 10274
 
3.3%
4 8457
 
2.7%
5 7306
 
2.3%
7 5665
 
1.8%
6 5551
 
1.8%
9 4590
 
1.5%
8 4568
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310923
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
71.1%
1 27900
 
9.0%
2 11220
 
3.6%
3 10274
 
3.3%
4 8457
 
2.7%
5 7306
 
2.3%
7 5665
 
1.8%
6 5551
 
1.8%
9 4590
 
1.5%
8 4568
 
1.5%

NPCHP7
Categorical

IMBALANCE 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
276887 
1
 
5150
2
 
4960
4
 
4196
3
 
4146
Other values (12)
 
6484

Length

Max length2
Median length1
Mean length1.0007455
Min length1

Characters and Unicode

Total characters302048
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row7
5th row

Common Values

ValueCountFrequency (%)
276887
91.7%
1 5150
 
1.7%
2 4960
 
1.6%
4 4196
 
1.4%
3 4146
 
1.4%
5 2906
 
1.0%
6 1164
 
0.4%
7 911
 
0.3%
0 847
 
0.3%
8 276
 
0.1%
Other values (7) 380
 
0.1%

Length

2024-05-06T23:47:15.112815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 5150
20.7%
2 4960
19.9%
4 4196
16.8%
3 4146
16.6%
5 2906
11.7%
6 1164
 
4.7%
7 911
 
3.7%
0 847
 
3.4%
8 276
 
1.1%
9 155
 
0.6%
Other values (6) 225
 
0.9%

Most occurring characters

ValueCountFrequency (%)
276887
91.7%
1 5407
 
1.8%
2 5002
 
1.7%
4 4208
 
1.4%
3 4155
 
1.4%
5 2931
 
1.0%
6 1164
 
0.4%
0 952
 
0.3%
7 911
 
0.3%
8 276
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 276887
91.7%
Decimal Number 25161
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5407
21.5%
2 5002
19.9%
4 4208
16.7%
3 4155
16.5%
5 2931
11.6%
6 1164
 
4.6%
0 952
 
3.8%
7 911
 
3.6%
8 276
 
1.1%
9 155
 
0.6%
Space Separator
ValueCountFrequency (%)
276887
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302048
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
276887
91.7%
1 5407
 
1.8%
2 5002
 
1.7%
4 4208
 
1.4%
3 4155
 
1.4%
5 2931
 
1.0%
6 1164
 
0.4%
0 952
 
0.3%
7 911
 
0.3%
8 276
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276887
91.7%
1 5407
 
1.8%
2 5002
 
1.7%
4 4208
 
1.4%
3 4155
 
1.4%
5 2931
 
1.0%
6 1164
 
0.4%
0 952
 
0.3%
7 911
 
0.3%
8 276
 
0.1%

NPCHP9A
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
265612 
2
35409 
1
 
802

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
265612
88.0%
2 35409
 
11.7%
1 802
 
0.3%

Length

2024-05-06T23:47:15.221316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:15.314780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 35409
97.8%
1 802
 
2.2%

Most occurring characters

ValueCountFrequency (%)
265612
88.0%
2 35409
 
11.7%
1 802
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 265612
88.0%
Decimal Number 36211
 
12.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35409
97.8%
1 802
 
2.2%
Space Separator
ValueCountFrequency (%)
265612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
265612
88.0%
2 35409
 
11.7%
1 802
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
265612
88.0%
2 35409
 
11.7%
1 802
 
0.3%

NPCHP9B
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
265612 
2
35527 
1
 
684

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
265612
88.0%
2 35527
 
11.8%
1 684
 
0.2%

Length

2024-05-06T23:47:15.424649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:15.523703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 35527
98.1%
1 684
 
1.9%

Most occurring characters

ValueCountFrequency (%)
265612
88.0%
2 35527
 
11.8%
1 684
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 265612
88.0%
Decimal Number 36211
 
12.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35527
98.1%
1 684
 
1.9%
Space Separator
ValueCountFrequency (%)
265612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
265612
88.0%
2 35527
 
11.8%
1 684
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
265612
88.0%
2 35527
 
11.8%
1 684
 
0.2%

NPCHP9C
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
2
52371 
1
 
3427

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
2 52371
 
17.4%
1 3427
 
1.1%

Length

2024-05-06T23:47:15.626326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:15.717933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 52371
93.9%
1 3427
 
6.1%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
2 52371
 
17.4%
1 3427
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52371
93.9%
1 3427
 
6.1%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
2 52371
 
17.4%
1 3427
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
2 52371
 
17.4%
1 3427
 
1.1%

NPCHP9D
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
282236 
2
 
19455
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
282236
93.5%
2 19455
 
6.4%
1 132
 
< 0.1%

Length

2024-05-06T23:47:15.816503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:15.905277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 19455
99.3%
1 132
 
0.7%

Most occurring characters

ValueCountFrequency (%)
282236
93.5%
2 19455
 
6.4%
1 132
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 282236
93.5%
Decimal Number 19587
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19455
99.3%
1 132
 
0.7%
Space Separator
ValueCountFrequency (%)
282236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
282236
93.5%
2 19455
 
6.4%
1 132
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282236
93.5%
2 19455
 
6.4%
1 132
 
< 0.1%

NPCHP9E
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
282236 
2
 
19297
1
 
290

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
282236
93.5%
2 19297
 
6.4%
1 290
 
0.1%

Length

2024-05-06T23:47:16.003256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:16.098495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 19297
98.5%
1 290
 
1.5%

Most occurring characters

ValueCountFrequency (%)
282236
93.5%
2 19297
 
6.4%
1 290
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 282236
93.5%
Decimal Number 19587
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19297
98.5%
1 290
 
1.5%
Space Separator
ValueCountFrequency (%)
282236
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
282236
93.5%
2 19297
 
6.4%
1 290
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282236
93.5%
2 19297
 
6.4%
1 290
 
0.1%

NPCHP9F
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
2
55581 
1
 
217

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
2 55581
 
18.4%
1 217
 
0.1%

Length

2024-05-06T23:47:16.199703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:16.298125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 55581
99.6%
1 217
 
0.4%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
2 55581
 
18.4%
1 217
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 55581
99.6%
1 217
 
0.4%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
2 55581
 
18.4%
1 217
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
2 55581
 
18.4%
1 217
 
0.1%

NPCHP10
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
2
39396 
1
 
16402

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
2 39396
 
13.1%
1 16402
 
5.4%

Length

2024-05-06T23:47:16.401323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:16.494853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 39396
70.6%
1 16402
29.4%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
2 39396
 
13.1%
1 16402
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 39396
70.6%
1 16402
29.4%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
2 39396
 
13.1%
1 16402
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
2 39396
 
13.1%
1 16402
 
5.4%

NPCHP10A
Text

Distinct960
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:16.727503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.2678855
Min length1

Characters and Unicode

Total characters382677
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique435 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
150000 785
 
4.8%
120000 657
 
4.0%
200000 592
 
3.6%
100000 539
 
3.3%
130000 498
 
3.0%
300000 456
 
2.8%
250000 420
 
2.6%
180000 411
 
2.5%
140000 338
 
2.1%
400000 311
 
1.9%
Other values (949) 11395
69.5%
2024-05-06T23:47:17.135666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
285421
74.6%
0 65725
 
17.2%
1 8474
 
2.2%
2 4953
 
1.3%
5 4945
 
1.3%
3 3176
 
0.8%
8 2418
 
0.6%
4 2142
 
0.6%
6 2003
 
0.5%
7 1842
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 285421
74.6%
Decimal Number 97256
 
25.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 65725
67.6%
1 8474
 
8.7%
2 4953
 
5.1%
5 4945
 
5.1%
3 3176
 
3.3%
8 2418
 
2.5%
4 2142
 
2.2%
6 2003
 
2.1%
7 1842
 
1.9%
9 1578
 
1.6%
Space Separator
ValueCountFrequency (%)
285421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 382677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
285421
74.6%
0 65725
 
17.2%
1 8474
 
2.2%
2 4953
 
1.3%
5 4945
 
1.3%
3 3176
 
0.8%
8 2418
 
0.6%
4 2142
 
0.6%
6 2003
 
0.5%
7 1842
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 382677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285421
74.6%
0 65725
 
17.2%
1 8474
 
2.2%
2 4953
 
1.3%
5 4945
 
1.3%
3 3176
 
0.8%
8 2418
 
0.6%
4 2142
 
0.6%
6 2003
 
0.5%
7 1842
 
0.5%

NPCHP11
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
2
47350 
1
 
8448

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
2 47350
 
15.7%
1 8448
 
2.8%

Length

2024-05-06T23:47:17.315789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:17.412753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 47350
84.9%
1 8448
 
15.1%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
2 47350
 
15.7%
1 8448
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 47350
84.9%
1 8448
 
15.1%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
2 47350
 
15.7%
1 8448
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
2 47350
 
15.7%
1 8448
 
2.8%

NPCHP11A
Text

Distinct305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:17.658676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.1279757
Min length1

Characters and Unicode

Total characters340449
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
100000 637
 
7.5%
200000 626
 
7.4%
150000 556
 
6.6%
120000 523
 
6.2%
80000 404
 
4.8%
300000 383
 
4.5%
60000 350
 
4.1%
250000 329
 
3.9%
90000 250
 
3.0%
98 247
 
2.9%
Other values (294) 4143
49.0%
2024-05-06T23:47:18.084507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
293375
86.2%
0 32950
 
9.7%
1 3797
 
1.1%
2 2642
 
0.8%
5 2070
 
0.6%
8 1267
 
0.4%
3 1160
 
0.3%
9 957
 
0.3%
6 862
 
0.3%
4 782
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 293375
86.2%
Decimal Number 47074
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32950
70.0%
1 3797
 
8.1%
2 2642
 
5.6%
5 2070
 
4.4%
8 1267
 
2.7%
3 1160
 
2.5%
9 957
 
2.0%
6 862
 
1.8%
4 782
 
1.7%
7 587
 
1.2%
Space Separator
ValueCountFrequency (%)
293375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 340449
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
293375
86.2%
0 32950
 
9.7%
1 3797
 
1.1%
2 2642
 
0.8%
5 2070
 
0.6%
8 1267
 
0.4%
3 1160
 
0.3%
9 957
 
0.3%
6 862
 
0.3%
4 782
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 340449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
293375
86.2%
0 32950
 
9.7%
1 3797
 
1.1%
2 2642
 
0.8%
5 2070
 
0.6%
8 1267
 
0.4%
3 1160
 
0.3%
9 957
 
0.3%
6 862
 
0.3%
4 782
 
0.2%

NPCHP12
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
1
47994 
2
32740 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
1 47994
 
15.9%
2 32740
 
10.8%

Length

2024-05-06T23:47:18.237591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:18.333904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 47994
59.4%
2 32740
40.6%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
1 47994
 
15.9%
2 32740
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 47994
59.4%
2 32740
40.6%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
1 47994
 
15.9%
2 32740
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
1 47994
 
15.9%
2 32740
 
10.8%

NPCHP12A
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
269083 
2
31313 
1
 
1427

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
269083
89.2%
2 31313
 
10.4%
1 1427
 
0.5%

Length

2024-05-06T23:47:18.440760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:18.536534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 31313
95.6%
1 1427
 
4.4%

Most occurring characters

ValueCountFrequency (%)
269083
89.2%
2 31313
 
10.4%
1 1427
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 269083
89.2%
Decimal Number 32740
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 31313
95.6%
1 1427
 
4.4%
Space Separator
ValueCountFrequency (%)
269083
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
269083
89.2%
2 31313
 
10.4%
1 1427
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269083
89.2%
2 31313
 
10.4%
1 1427
 
0.5%

NPCHP13
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
1
53397 
2
 
2401

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
1 53397
 
17.7%
2 2401
 
0.8%

Length

2024-05-06T23:47:18.637409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:18.732143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 53397
95.7%
2 2401
 
4.3%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
1 53397
 
17.7%
2 2401
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 53397
95.7%
2 2401
 
4.3%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
1 53397
 
17.7%
2 2401
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
1 53397
 
17.7%
2 2401
 
0.8%

NPCHP13A
Categorical

IMBALANCE 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
299422 
25
 
1992
11
 
306
68
 
28
15
 
17
Other values (15)
 
58

Length

Max length2
Median length1
Mean length1.007955
Min length1

Characters and Unicode

Total characters304224
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
299422
99.2%
25 1992
 
0.7%
11 306
 
0.1%
68 28
 
< 0.1%
15 17
 
< 0.1%
73 15
 
< 0.1%
50 10
 
< 0.1%
17 8
 
< 0.1%
05 5
 
< 0.1%
76 4
 
< 0.1%
Other values (10) 16
 
< 0.1%

Length

2024-05-06T23:47:18.853160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
25 1992
83.0%
11 306
 
12.7%
68 28
 
1.2%
15 17
 
0.7%
73 15
 
0.6%
50 10
 
0.4%
17 8
 
0.3%
05 5
 
0.2%
76 4
 
0.2%
54 3
 
0.1%
Other values (9) 13
 
0.5%

Most occurring characters

ValueCountFrequency (%)
299422
98.4%
5 2029
 
0.7%
2 1995
 
0.7%
1 641
 
0.2%
6 34
 
< 0.1%
7 31
 
< 0.1%
8 29
 
< 0.1%
0 19
 
< 0.1%
3 16
 
< 0.1%
4 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299422
98.4%
Decimal Number 4802
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2029
42.3%
2 1995
41.5%
1 641
 
13.3%
6 34
 
0.7%
7 31
 
0.6%
8 29
 
0.6%
0 19
 
0.4%
3 16
 
0.3%
4 6
 
0.1%
9 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
299422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304224
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299422
98.4%
5 2029
 
0.7%
2 1995
 
0.7%
1 641
 
0.2%
6 34
 
< 0.1%
7 31
 
< 0.1%
8 29
 
< 0.1%
0 19
 
< 0.1%
3 16
 
< 0.1%
4 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299422
98.4%
5 2029
 
0.7%
2 1995
 
0.7%
1 641
 
0.2%
6 34
 
< 0.1%
7 31
 
< 0.1%
8 29
 
< 0.1%
0 19
 
< 0.1%
3 16
 
< 0.1%
4 6
 
< 0.1%

NPCHP13B
Text

Distinct118
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:19.009467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.03182
Min length1

Characters and Unicode

Total characters311427
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
25214 358
14.9%
25175 353
14.7%
11001 306
12.7%
25899 137
 
5.7%
25377 129
 
5.4%
25473 97
 
4.0%
25817 82
 
3.4%
25873 76
 
3.2%
25269 74
 
3.1%
25799 71
 
3.0%
Other values (107) 718
29.9%
2024-05-06T23:47:19.320371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
299422
96.1%
2 2721
 
0.9%
5 2550
 
0.8%
1 1972
 
0.6%
7 1221
 
0.4%
0 840
 
0.3%
4 632
 
0.2%
8 603
 
0.2%
9 595
 
0.2%
3 508
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299422
96.1%
Decimal Number 12005
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2721
22.7%
5 2550
21.2%
1 1972
16.4%
7 1221
10.2%
0 840
 
7.0%
4 632
 
5.3%
8 603
 
5.0%
9 595
 
5.0%
3 508
 
4.2%
6 363
 
3.0%
Space Separator
ValueCountFrequency (%)
299422
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 311427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299422
96.1%
2 2721
 
0.9%
5 2550
 
0.8%
1 1972
 
0.6%
7 1221
 
0.4%
0 840
 
0.3%
4 632
 
0.2%
8 603
 
0.2%
9 595
 
0.2%
3 508
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 311427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299422
96.1%
2 2721
 
0.9%
5 2550
 
0.8%
1 1972
 
0.6%
7 1221
 
0.4%
0 840
 
0.3%
4 632
 
0.2%
8 603
 
0.2%
9 595
 
0.2%
3 508
 
0.2%

NPCHP14
Categorical

IMBALANCE 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
266794 
08
 
4291
05
 
3235
11
 
2830
19
 
2598
Other values (17)
 
22075

Length

Max length2
Median length1
Mean length1.1160581
Min length1

Characters and Unicode

Total characters336852
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
266794
88.4%
08 4291
 
1.4%
05 3235
 
1.1%
11 2830
 
0.9%
19 2598
 
0.9%
07 2587
 
0.9%
10 2352
 
0.8%
09 2237
 
0.7%
04 2170
 
0.7%
18 1963
 
0.7%
Other values (12) 10766
 
3.6%

Length

2024-05-06T23:47:19.469924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
08 4291
12.2%
05 3235
 
9.2%
11 2830
 
8.1%
19 2598
 
7.4%
07 2587
 
7.4%
10 2352
 
6.7%
09 2237
 
6.4%
04 2170
 
6.2%
18 1963
 
5.6%
01 1924
 
5.5%
Other values (11) 8842
25.2%

Most occurring characters

ValueCountFrequency (%)
266794
79.2%
0 22163
 
6.6%
1 18956
 
5.6%
9 6867
 
2.0%
8 6254
 
1.9%
5 4011
 
1.2%
6 2950
 
0.9%
7 2936
 
0.9%
4 2903
 
0.9%
2 1559
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 266794
79.2%
Decimal Number 70058
 
20.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22163
31.6%
1 18956
27.1%
9 6867
 
9.8%
8 6254
 
8.9%
5 4011
 
5.7%
6 2950
 
4.2%
7 2936
 
4.2%
4 2903
 
4.1%
2 1559
 
2.2%
3 1459
 
2.1%
Space Separator
ValueCountFrequency (%)
266794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 336852
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
266794
79.2%
0 22163
 
6.6%
1 18956
 
5.6%
9 6867
 
2.0%
8 6254
 
1.9%
5 4011
 
1.2%
6 2950
 
0.9%
7 2936
 
0.9%
4 2903
 
0.9%
2 1559
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 336852
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
266794
79.2%
0 22163
 
6.6%
1 18956
 
5.6%
9 6867
 
2.0%
8 6254
 
1.9%
5 4011
 
1.2%
6 2950
 
0.9%
7 2936
 
0.9%
4 2903
 
0.9%
2 1559
 
0.5%

NPCHP15A
Text

Distinct692
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:19.636209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.6952916
Min length1

Characters and Unicode

Total characters511678
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
98 2978
 
13.7%
211850000876 240
 
1.1%
111279000168 176
 
0.8%
111001024643 174
 
0.8%
111001013676 166
 
0.8%
211001032501 159
 
0.7%
111001041556 150
 
0.7%
111001104281 139
 
0.6%
111001098833 132
 
0.6%
111279000061 132
 
0.6%
Other values (681) 17339
79.6%
2024-05-06T23:47:19.966627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
280038
54.7%
1 89198
 
17.4%
0 66251
 
12.9%
8 12458
 
2.4%
2 11559
 
2.3%
9 9971
 
1.9%
6 8831
 
1.7%
3 8813
 
1.7%
4 8785
 
1.7%
5 7951
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 280038
54.7%
Decimal Number 231640
45.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 89198
38.5%
0 66251
28.6%
8 12458
 
5.4%
2 11559
 
5.0%
9 9971
 
4.3%
6 8831
 
3.8%
3 8813
 
3.8%
4 8785
 
3.8%
5 7951
 
3.4%
7 7823
 
3.4%
Space Separator
ValueCountFrequency (%)
280038
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 511678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
280038
54.7%
1 89198
 
17.4%
0 66251
 
12.9%
8 12458
 
2.4%
2 11559
 
2.3%
9 9971
 
1.9%
6 8831
 
1.7%
3 8813
 
1.7%
4 8785
 
1.7%
5 7951
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 511678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
280038
54.7%
1 89198
 
17.4%
0 66251
 
12.9%
8 12458
 
2.4%
2 11559
 
2.3%
9 9971
 
1.9%
6 8831
 
1.7%
3 8813
 
1.7%
4 8785
 
1.7%
5 7951
 
1.6%

NPCHP15B
Text

Distinct1434
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:20.167798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.3787915
Min length1

Characters and Unicode

Total characters416151
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique305 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
98 2018
 
16.5%
311001093652 107
 
0.9%
311001019053 85
 
0.7%
311848000308 73
 
0.6%
311001001901 66
 
0.5%
311279000281 56
 
0.5%
311001004986 54
 
0.4%
311001004820 53
 
0.4%
311001001057 51
 
0.4%
311001108919 50
 
0.4%
Other values (1423) 9615
78.6%
2024-05-06T23:47:20.482125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
289595
69.6%
0 36642
 
8.8%
1 36228
 
8.7%
3 15302
 
3.7%
9 7977
 
1.9%
8 7615
 
1.8%
7 4986
 
1.2%
2 4942
 
1.2%
4 4913
 
1.2%
6 4472
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 289595
69.6%
Decimal Number 126556
30.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36642
29.0%
1 36228
28.6%
3 15302
12.1%
9 7977
 
6.3%
8 7615
 
6.0%
7 4986
 
3.9%
2 4942
 
3.9%
4 4913
 
3.9%
6 4472
 
3.5%
5 3479
 
2.7%
Space Separator
ValueCountFrequency (%)
289595
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 416151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
289595
69.6%
0 36642
 
8.8%
1 36228
 
8.7%
3 15302
 
3.7%
9 7977
 
1.9%
8 7615
 
1.8%
7 4986
 
1.2%
2 4942
 
1.2%
4 4913
 
1.2%
6 4472
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 416151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289595
69.6%
0 36642
 
8.8%
1 36228
 
8.7%
3 15302
 
3.7%
9 7977
 
1.9%
8 7615
 
1.8%
7 4986
 
1.2%
2 4942
 
1.2%
4 4913
 
1.2%
6 4472
 
1.1%

NPCHP16
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
280038 
1
 
20291
2
 
1494

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
280038
92.8%
1 20291
 
6.7%
2 1494
 
0.5%

Length

2024-05-06T23:47:20.638505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:20.728582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 20291
93.1%
2 1494
 
6.9%

Most occurring characters

ValueCountFrequency (%)
280038
92.8%
1 20291
 
6.7%
2 1494
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 280038
92.8%
Decimal Number 21785
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20291
93.1%
2 1494
 
6.9%
Space Separator
ValueCountFrequency (%)
280038
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
280038
92.8%
1 20291
 
6.7%
2 1494
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
280038
92.8%
1 20291
 
6.7%
2 1494
 
0.5%

NPCHP17
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
246025 
1
36734 
4
 
11421
2
 
6491
3
 
611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
246025
81.5%
1 36734
 
12.2%
4 11421
 
3.8%
2 6491
 
2.2%
3 611
 
0.2%
5 541
 
0.2%

Length

2024-05-06T23:47:21.299196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:21.403113image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 36734
65.8%
4 11421
 
20.5%
2 6491
 
11.6%
3 611
 
1.1%
5 541
 
1.0%

Most occurring characters

ValueCountFrequency (%)
246025
81.5%
1 36734
 
12.2%
4 11421
 
3.8%
2 6491
 
2.2%
3 611
 
0.2%
5 541
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 246025
81.5%
Decimal Number 55798
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36734
65.8%
4 11421
 
20.5%
2 6491
 
11.6%
3 611
 
1.1%
5 541
 
1.0%
Space Separator
ValueCountFrequency (%)
246025
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
246025
81.5%
1 36734
 
12.2%
4 11421
 
3.8%
2 6491
 
2.2%
3 611
 
0.2%
5 541
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
246025
81.5%
1 36734
 
12.2%
4 11421
 
3.8%
2 6491
 
2.2%
3 611
 
0.2%
5 541
 
0.2%

NPCHP18A
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
288493 
1
 
13330

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
288493
95.6%
1 13330
 
4.4%

Length

2024-05-06T23:47:21.515279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:21.606120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 13330
100.0%

Most occurring characters

ValueCountFrequency (%)
288493
95.6%
1 13330
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 288493
95.6%
Decimal Number 13330
 
4.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
288493
100.0%
Decimal Number
ValueCountFrequency (%)
1 13330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
288493
95.6%
1 13330
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288493
95.6%
1 13330
 
4.4%

NPCHP18B
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
294611 
1
 
7212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
294611
97.6%
1 7212
 
2.4%

Length

2024-05-06T23:47:21.702789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:21.791061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 7212
100.0%

Most occurring characters

ValueCountFrequency (%)
294611
97.6%
1 7212
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 294611
97.6%
Decimal Number 7212
 
2.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
294611
100.0%
Decimal Number
ValueCountFrequency (%)
1 7212
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
294611
97.6%
1 7212
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294611
97.6%
1 7212
 
2.4%

NPCHP18C
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
292911 
1
 
8912

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
292911
97.0%
1 8912
 
3.0%

Length

2024-05-06T23:47:21.890675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:21.986518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 8912
100.0%

Most occurring characters

ValueCountFrequency (%)
292911
97.0%
1 8912
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 292911
97.0%
Decimal Number 8912
 
3.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
292911
100.0%
Decimal Number
ValueCountFrequency (%)
1 8912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
292911
97.0%
1 8912
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
292911
97.0%
1 8912
 
3.0%

NPCHP18D
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
297865 
1
 
3958

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
297865
98.7%
1 3958
 
1.3%

Length

2024-05-06T23:47:22.086246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:22.177080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3958
100.0%

Most occurring characters

ValueCountFrequency (%)
297865
98.7%
1 3958
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 297865
98.7%
Decimal Number 3958
 
1.3%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
297865
100.0%
Decimal Number
ValueCountFrequency (%)
1 3958
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
297865
98.7%
1 3958
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
297865
98.7%
1 3958
 
1.3%

NPCHP18E
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300500 
1
 
1323

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
300500
99.6%
1 1323
 
0.4%

Length

2024-05-06T23:47:22.269601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:22.357471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1323
100.0%

Most occurring characters

ValueCountFrequency (%)
300500
99.6%
1 1323
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300500
99.6%
Decimal Number 1323
 
0.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
300500
100.0%
Decimal Number
ValueCountFrequency (%)
1 1323
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300500
99.6%
1 1323
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300500
99.6%
1 1323
 
0.4%

NPCHP18F
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
299679 
1
 
2144

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
299679
99.3%
1 2144
 
0.7%

Length

2024-05-06T23:47:22.455963image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:22.546088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2144
100.0%

Most occurring characters

ValueCountFrequency (%)
299679
99.3%
1 2144
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299679
99.3%
Decimal Number 2144
 
0.7%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
299679
100.0%
Decimal Number
ValueCountFrequency (%)
1 2144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299679
99.3%
1 2144
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299679
99.3%
1 2144
 
0.7%

NPCHP18G
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
298742 
1
 
3081

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
298742
99.0%
1 3081
 
1.0%

Length

2024-05-06T23:47:22.641468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:22.728076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 3081
100.0%

Most occurring characters

ValueCountFrequency (%)
298742
99.0%
1 3081
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 298742
99.0%
Decimal Number 3081
 
1.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
298742
100.0%
Decimal Number
ValueCountFrequency (%)
1 3081
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
298742
99.0%
1 3081
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298742
99.0%
1 3081
 
1.0%

NPCHP18H
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
291962 
1
 
9861

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
291962
96.7%
1 9861
 
3.3%

Length

2024-05-06T23:47:22.821841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:22.913864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 9861
100.0%

Most occurring characters

ValueCountFrequency (%)
291962
96.7%
1 9861
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 291962
96.7%
Decimal Number 9861
 
3.3%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
291962
100.0%
Decimal Number
ValueCountFrequency (%)
1 9861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
291962
96.7%
1 9861
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
291962
96.7%
1 9861
 
3.3%

NPCHP18I
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
262819 
1
39004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
262819
87.1%
1 39004
 
12.9%

Length

2024-05-06T23:47:23.012806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:23.107724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 39004
100.0%

Most occurring characters

ValueCountFrequency (%)
262819
87.1%
1 39004
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 262819
87.1%
Decimal Number 39004
 
12.9%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
262819
100.0%
Decimal Number
ValueCountFrequency (%)
1 39004
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
262819
87.1%
1 39004
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262819
87.1%
1 39004
 
12.9%

NPCHP18J
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
299546 
1
 
2277

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
299546
99.2%
1 2277
 
0.8%

Length

2024-05-06T23:47:23.214140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:23.306718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2277
100.0%

Most occurring characters

ValueCountFrequency (%)
299546
99.2%
1 2277
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299546
99.2%
Decimal Number 2277
 
0.8%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
299546
100.0%
Decimal Number
ValueCountFrequency (%)
1 2277
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299546
99.2%
1 2277
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299546
99.2%
1 2277
 
0.8%

NPCHP18K
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301711 
1
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301711
> 99.9%
1 112
 
< 0.1%

Length

2024-05-06T23:47:23.405116image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:23.497365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 112
100.0%

Most occurring characters

ValueCountFrequency (%)
301711
> 99.9%
1 112
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301711
> 99.9%
Decimal Number 112
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301711
100.0%
Decimal Number
ValueCountFrequency (%)
1 112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301711
> 99.9%
1 112
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301711
> 99.9%
1 112
 
< 0.1%

NPCHP18L
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301813 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301813
> 99.9%
1 10
 
< 0.1%

Length

2024-05-06T23:47:23.599756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:23.693525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 10
100.0%

Most occurring characters

ValueCountFrequency (%)
301813
> 99.9%
1 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301813
> 99.9%
Decimal Number 10
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301813
100.0%
Decimal Number
ValueCountFrequency (%)
1 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301813
> 99.9%
1 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301813
> 99.9%
1 10
 
< 0.1%

NPCHP18M
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300694 
1
 
1129

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
300694
99.6%
1 1129
 
0.4%

Length

2024-05-06T23:47:23.795150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:23.885491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1129
100.0%

Most occurring characters

ValueCountFrequency (%)
300694
99.6%
1 1129
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300694
99.6%
Decimal Number 1129
 
0.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
300694
100.0%
Decimal Number
ValueCountFrequency (%)
1 1129
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300694
99.6%
1 1129
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300694
99.6%
1 1129
 
0.4%

NPCHP19B
Text

Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:24.027428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.2416814
Min length1

Characters and Unicode

Total characters374768
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row1
5th row
ValueCountFrequency (%)
10 13166
16.3%
15 11919
14.8%
20 10636
13.2%
30 8967
11.1%
60 6709
8.3%
5 6494
8.0%
40 4523
 
5.6%
45 2949
 
3.7%
90 2481
 
3.1%
120 1870
 
2.3%
Other values (100) 11020
13.6%
2024-05-06T23:47:24.348108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
221089
59.0%
0 51023
 
13.6%
1 29471
 
7.9%
5 25573
 
6.8%
2 15494
 
4.1%
3 10892
 
2.9%
4 8096
 
2.2%
6 7522
 
2.0%
9 3077
 
0.8%
7 1284
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
59.0%
Decimal Number 153679
41.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51023
33.2%
1 29471
19.2%
5 25573
16.6%
2 15494
 
10.1%
3 10892
 
7.1%
4 8096
 
5.3%
6 7522
 
4.9%
9 3077
 
2.0%
7 1284
 
0.8%
8 1247
 
0.8%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 374768
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
59.0%
0 51023
 
13.6%
1 29471
 
7.9%
5 25573
 
6.8%
2 15494
 
4.1%
3 10892
 
2.9%
4 8096
 
2.2%
6 7522
 
2.0%
9 3077
 
0.8%
7 1284
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
59.0%
0 51023
 
13.6%
1 29471
 
7.9%
5 25573
 
6.8%
2 15494
 
4.1%
3 10892
 
2.9%
4 8096
 
2.2%
6 7522
 
2.0%
9 3077
 
0.8%
7 1284
 
0.3%

NPCHP20
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
61785 
1
 
18949

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 61785
 
20.5%
1 18949
 
6.3%

Length

2024-05-06T23:47:24.487310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:24.579431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 61785
76.5%
1 18949
 
23.5%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 61785
 
20.5%
1 18949
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 61785
76.5%
1 18949
 
23.5%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 61785
 
20.5%
1 18949
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 61785
 
20.5%
1 18949
 
6.3%

NPCHP20A
Text

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:24.675011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.0364518
Min length1

Characters and Unicode

Total characters312825
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
0 14527
76.7%
200 1328
 
7.0%
5000 587
 
3.1%
2000 581
 
3.1%
100 332
 
1.8%
1000 256
 
1.4%
3000 222
 
1.2%
500 146
 
0.8%
4000 137
 
0.7%
300 114
 
0.6%
Other values (53) 719
 
3.8%
2024-05-06T23:47:24.940430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282874
90.4%
0 25008
 
8.0%
2 2115
 
0.7%
5 1116
 
0.4%
1 751
 
0.2%
3 478
 
0.2%
4 303
 
0.1%
9 91
 
< 0.1%
8 45
 
< 0.1%
7 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 282874
90.4%
Decimal Number 29951
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25008
83.5%
2 2115
 
7.1%
5 1116
 
3.7%
1 751
 
2.5%
3 478
 
1.6%
4 303
 
1.0%
9 91
 
0.3%
8 45
 
0.2%
7 33
 
0.1%
6 11
 
< 0.1%
Space Separator
ValueCountFrequency (%)
282874
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
282874
90.4%
0 25008
 
8.0%
2 2115
 
0.7%
5 1116
 
0.4%
1 751
 
0.2%
3 478
 
0.2%
4 303
 
0.1%
9 91
 
< 0.1%
8 45
 
< 0.1%
7 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282874
90.4%
0 25008
 
8.0%
2 2115
 
0.7%
5 1116
 
0.4%
1 751
 
0.2%
3 478
 
0.2%
4 303
 
0.1%
9 91
 
< 0.1%
8 45
 
< 0.1%
7 33
 
< 0.1%

NPCHP20B
Text

Distinct127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:25.134513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.1913274
Min length1

Characters and Unicode

Total characters359570
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
2000 3873
20.4%
3000 3828
20.2%
5000 2182
11.5%
4000 1319
 
7.0%
1000 991
 
5.2%
2500 882
 
4.7%
10000 836
 
4.4%
6000 671
 
3.5%
20000 600
 
3.2%
200 567
 
3.0%
Other values (116) 3200
16.9%
2024-05-06T23:47:25.461305image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
282874
78.7%
0 54861
 
15.3%
2 6379
 
1.8%
5 4508
 
1.3%
3 4380
 
1.2%
1 3085
 
0.9%
4 1559
 
0.4%
6 816
 
0.2%
7 533
 
0.1%
8 416
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 282874
78.7%
Decimal Number 76696
 
21.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54861
71.5%
2 6379
 
8.3%
5 4508
 
5.9%
3 4380
 
5.7%
1 3085
 
4.0%
4 1559
 
2.0%
6 816
 
1.1%
7 533
 
0.7%
8 416
 
0.5%
9 159
 
0.2%
Space Separator
ValueCountFrequency (%)
282874
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 359570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
282874
78.7%
0 54861
 
15.3%
2 6379
 
1.8%
5 4508
 
1.3%
3 4380
 
1.2%
1 3085
 
0.9%
4 1559
 
0.4%
6 816
 
0.2%
7 533
 
0.1%
8 416
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 359570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
282874
78.7%
0 54861
 
15.3%
2 6379
 
1.8%
5 4508
 
1.3%
3 4380
 
1.2%
1 3085
 
0.9%
4 1559
 
0.4%
6 816
 
0.2%
7 533
 
0.1%
8 416
 
0.1%

NPCHP21A
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
41539 
1
39195 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row1
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 41539
 
13.8%
1 39195
 
13.0%

Length

2024-05-06T23:47:25.602204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:25.700246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 41539
51.5%
1 39195
48.5%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 41539
 
13.8%
1 39195
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 41539
51.5%
1 39195
48.5%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 41539
 
13.8%
1 39195
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 41539
 
13.8%
1 39195
 
13.0%

NPCHP21AA
Text

Distinct1383
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:25.953657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.7005596
Min length1

Characters and Unicode

Total characters513268
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique689 ?
Unique (%)0.2%

Sample

1st row
2nd row
3rd row
4th row17000000
5th row
ValueCountFrequency (%)
300000 1627
 
4.2%
500000 1459
 
3.7%
400000 1310
 
3.3%
600000 1116
 
2.8%
1000000 1092
 
2.8%
200000 1018
 
2.6%
800000 913
 
2.3%
1200000 911
 
2.3%
3000000 895
 
2.3%
2000000 884
 
2.3%
Other values (1372) 27970
71.4%
2024-05-06T23:47:26.396666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
262628
51.2%
0 186090
36.3%
5 11759
 
2.3%
1 11706
 
2.3%
2 10124
 
2.0%
3 8321
 
1.6%
4 6184
 
1.2%
8 5090
 
1.0%
6 4726
 
0.9%
7 3760
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 262628
51.2%
Decimal Number 250640
48.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 186090
74.2%
5 11759
 
4.7%
1 11706
 
4.7%
2 10124
 
4.0%
3 8321
 
3.3%
4 6184
 
2.5%
8 5090
 
2.0%
6 4726
 
1.9%
7 3760
 
1.5%
9 2880
 
1.1%
Space Separator
ValueCountFrequency (%)
262628
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 513268
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
262628
51.2%
0 186090
36.3%
5 11759
 
2.3%
1 11706
 
2.3%
2 10124
 
2.0%
3 8321
 
1.6%
4 6184
 
1.2%
8 5090
 
1.0%
6 4726
 
0.9%
7 3760
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262628
51.2%
0 186090
36.3%
5 11759
 
2.3%
1 11706
 
2.3%
2 10124
 
2.0%
3 8321
 
1.6%
4 6184
 
1.2%
8 5090
 
1.0%
6 4726
 
0.9%
7 3760
 
0.7%

NPCHP21B
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
1
60647 
2
 
20087

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
1 60647
 
20.1%
2 20087
 
6.7%

Length

2024-05-06T23:47:26.554575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:26.653185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 60647
75.1%
2 20087
 
24.9%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
1 60647
 
20.1%
2 20087
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 60647
75.1%
2 20087
 
24.9%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
1 60647
 
20.1%
2 20087
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
1 60647
 
20.1%
2 20087
 
6.7%

NPCHP21BA
Text

Distinct429
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:26.926941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.9865053
Min length1

Characters and Unicode

Total characters599573
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
200000 8072
13.3%
300000 7465
 
12.3%
100000 5247
 
8.7%
500000 4378
 
7.2%
150000 4232
 
7.0%
400000 3477
 
5.7%
250000 2849
 
4.7%
50000 2166
 
3.6%
1000000 2080
 
3.4%
600000 1726
 
2.8%
Other values (418) 18955
31.3%
2024-05-06T23:47:27.388003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 279387
46.6%
241176
40.2%
5 17870
 
3.0%
1 17372
 
2.9%
2 16026
 
2.7%
3 11511
 
1.9%
4 5504
 
0.9%
6 3677
 
0.6%
8 3565
 
0.6%
7 2095
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358397
59.8%
Space Separator 241176
40.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 279387
78.0%
5 17870
 
5.0%
1 17372
 
4.8%
2 16026
 
4.5%
3 11511
 
3.2%
4 5504
 
1.5%
6 3677
 
1.0%
8 3565
 
1.0%
7 2095
 
0.6%
9 1390
 
0.4%
Space Separator
ValueCountFrequency (%)
241176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 599573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 279387
46.6%
241176
40.2%
5 17870
 
3.0%
1 17372
 
2.9%
2 16026
 
2.7%
3 11511
 
1.9%
4 5504
 
0.9%
6 3677
 
0.6%
8 3565
 
0.6%
7 2095
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 599573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 279387
46.6%
241176
40.2%
5 17870
 
3.0%
1 17372
 
2.9%
2 16026
 
2.7%
3 11511
 
1.9%
4 5504
 
0.9%
6 3677
 
0.6%
8 3565
 
0.6%
7 2095
 
0.3%

NPCHP21C
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
78577 
1
 
2157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 78577
 
26.0%
1 2157
 
0.7%

Length

2024-05-06T23:47:27.545921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:27.638690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 78577
97.3%
1 2157
 
2.7%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 78577
 
26.0%
1 2157
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 78577
97.3%
1 2157
 
2.7%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 78577
 
26.0%
1 2157
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 78577
 
26.0%
1 2157
 
0.7%

NPCHP21CA
Text

Distinct141
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:27.792131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.028858
Min length1

Characters and Unicode

Total characters310533
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
100000 210
 
9.7%
50000 206
 
9.6%
98 165
 
7.6%
200000 122
 
5.7%
30000 121
 
5.6%
99 93
 
4.3%
20000 83
 
3.8%
150000 78
 
3.6%
80000 75
 
3.5%
300000 72
 
3.3%
Other values (130) 932
43.2%
2024-05-06T23:47:28.108124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
299666
96.5%
0 7835
 
2.5%
1 611
 
0.2%
5 587
 
0.2%
2 470
 
0.2%
9 402
 
0.1%
8 302
 
0.1%
3 301
 
0.1%
4 161
 
0.1%
6 111
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299666
96.5%
Decimal Number 10867
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7835
72.1%
1 611
 
5.6%
5 587
 
5.4%
2 470
 
4.3%
9 402
 
3.7%
8 302
 
2.8%
3 301
 
2.8%
4 161
 
1.5%
6 111
 
1.0%
7 87
 
0.8%
Space Separator
ValueCountFrequency (%)
299666
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 310533
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299666
96.5%
0 7835
 
2.5%
1 611
 
0.2%
5 587
 
0.2%
2 470
 
0.2%
9 402
 
0.1%
8 302
 
0.1%
3 301
 
0.1%
4 161
 
0.1%
6 111
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310533
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299666
96.5%
0 7835
 
2.5%
1 611
 
0.2%
5 587
 
0.2%
2 470
 
0.2%
9 402
 
0.1%
8 302
 
0.1%
3 301
 
0.1%
4 161
 
0.1%
6 111
 
< 0.1%

NPCHP22
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
41531 
1
39203 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 41531
 
13.8%
1 39203
 
13.0%

Length

2024-05-06T23:47:28.263322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:28.361457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 41531
51.4%
1 39203
48.6%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 41531
 
13.8%
1 39203
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 41531
51.4%
1 39203
48.6%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 41531
 
13.8%
1 39203
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 41531
 
13.8%
1 39203
 
13.0%

NPCHP22A
Text

Distinct296
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:28.569803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.5312253
Min length1

Characters and Unicode

Total characters462159
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
20000 6052
15.4%
50000 5051
12.9%
10000 4805
12.3%
30000 4058
10.4%
100000 3114
 
7.9%
5000 1922
 
4.9%
15000 1591
 
4.1%
40000 1438
 
3.7%
200000 1375
 
3.5%
60000 1067
 
2.7%
Other values (285) 8730
22.3%
2024-05-06T23:47:28.940694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
262620
56.8%
0 154355
33.4%
1 11753
 
2.5%
5 11281
 
2.4%
2 9686
 
2.1%
3 5789
 
1.3%
4 2345
 
0.5%
6 1576
 
0.3%
8 1219
 
0.3%
7 920
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 262620
56.8%
Decimal Number 199539
43.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 154355
77.4%
1 11753
 
5.9%
5 11281
 
5.7%
2 9686
 
4.9%
3 5789
 
2.9%
4 2345
 
1.2%
6 1576
 
0.8%
8 1219
 
0.6%
7 920
 
0.5%
9 615
 
0.3%
Space Separator
ValueCountFrequency (%)
262620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 462159
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
262620
56.8%
0 154355
33.4%
1 11753
 
2.5%
5 11281
 
2.4%
2 9686
 
2.1%
3 5789
 
1.3%
4 2345
 
0.5%
6 1576
 
0.3%
8 1219
 
0.3%
7 920
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 462159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262620
56.8%
0 154355
33.4%
1 11753
 
2.5%
5 11281
 
2.4%
2 9686
 
2.1%
3 5789
 
1.3%
4 2345
 
0.5%
6 1576
 
0.3%
8 1219
 
0.3%
7 920
 
0.2%

NPCHP23
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
74866 
1
 
5868

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 74866
 
24.8%
1 5868
 
1.9%

Length

2024-05-06T23:47:29.090501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:29.186067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 74866
92.7%
1 5868
 
7.3%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 74866
 
24.8%
1 5868
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 74866
92.7%
1 5868
 
7.3%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 74866
 
24.8%
1 5868
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 74866
 
24.8%
1 5868
 
1.9%

NPCHP23A
Text

Distinct172
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:29.363339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length6
Median length1
Mean length1.0801993
Min length1

Characters and Unicode

Total characters326029
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
50000 637
 
10.9%
30000 501
 
8.5%
20000 501
 
8.5%
100000 407
 
6.9%
10000 350
 
6.0%
40000 326
 
5.6%
60000 245
 
4.2%
35000 205
 
3.5%
25000 202
 
3.4%
15000 177
 
3.0%
Other values (161) 2317
39.5%
2024-05-06T23:47:29.699999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
295955
90.8%
0 22413
 
6.9%
5 1979
 
0.6%
1 1511
 
0.5%
2 1281
 
0.4%
3 999
 
0.3%
4 607
 
0.2%
6 428
 
0.1%
7 320
 
0.1%
8 311
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 295955
90.8%
Decimal Number 30074
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22413
74.5%
5 1979
 
6.6%
1 1511
 
5.0%
2 1281
 
4.3%
3 999
 
3.3%
4 607
 
2.0%
6 428
 
1.4%
7 320
 
1.1%
8 311
 
1.0%
9 225
 
0.7%
Space Separator
ValueCountFrequency (%)
295955
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 326029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
295955
90.8%
0 22413
 
6.9%
5 1979
 
0.6%
1 1511
 
0.5%
2 1281
 
0.4%
3 999
 
0.3%
4 607
 
0.2%
6 428
 
0.1%
7 320
 
0.1%
8 311
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
295955
90.8%
0 22413
 
6.9%
5 1979
 
0.6%
1 1511
 
0.5%
2 1281
 
0.4%
3 999
 
0.3%
4 607
 
0.2%
6 428
 
0.1%
7 320
 
0.1%
8 311
 
0.1%

NPCHP24
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
78814 
1
 
1920

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 78814
 
26.1%
1 1920
 
0.6%

Length

2024-05-06T23:47:29.851206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:29.947134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 78814
97.6%
1 1920
 
2.4%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 78814
 
26.1%
1 1920
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 78814
97.6%
1 1920
 
2.4%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 78814
 
26.1%
1 1920
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 78814
 
26.1%
1 1920
 
0.6%

NPCHP24_1A
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300446 
1
 
1377

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
300446
99.5%
1 1377
 
0.5%

Length

2024-05-06T23:47:30.051663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:30.149322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1377
100.0%

Most occurring characters

ValueCountFrequency (%)
300446
99.5%
1 1377
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300446
99.5%
Decimal Number 1377
 
0.5%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
300446
100.0%
Decimal Number
ValueCountFrequency (%)
1 1377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300446
99.5%
1 1377
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300446
99.5%
1 1377
 
0.5%

NPCHP24AA
Text

Distinct222
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:30.324529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.0235767
Min length1

Characters and Unicode

Total characters308939
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique102 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
6000000 105
 
7.6%
1000000 53
 
3.8%
2000000 51
 
3.7%
400000 47
 
3.4%
300000 43
 
3.1%
3000000 39
 
2.8%
99 36
 
2.6%
4000000 36
 
2.6%
700000 35
 
2.5%
200000 33
 
2.4%
Other values (211) 899
65.3%
2024-05-06T23:47:30.679892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300446
97.3%
0 6297
 
2.0%
1 343
 
0.1%
2 341
 
0.1%
5 336
 
0.1%
3 295
 
0.1%
6 233
 
0.1%
4 200
 
0.1%
7 158
 
0.1%
8 148
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300446
97.3%
Decimal Number 8493
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6297
74.1%
1 343
 
4.0%
2 341
 
4.0%
5 336
 
4.0%
3 295
 
3.5%
6 233
 
2.7%
4 200
 
2.4%
7 158
 
1.9%
8 148
 
1.7%
9 142
 
1.7%
Space Separator
ValueCountFrequency (%)
300446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 308939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300446
97.3%
0 6297
 
2.0%
1 343
 
0.1%
2 341
 
0.1%
5 336
 
0.1%
3 295
 
0.1%
6 233
 
0.1%
4 200
 
0.1%
7 158
 
0.1%
8 148
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300446
97.3%
0 6297
 
2.0%
1 343
 
0.1%
2 341
 
0.1%
5 336
 
0.1%
3 295
 
0.1%
6 233
 
0.1%
4 200
 
0.1%
7 158
 
0.1%
8 148
 
< 0.1%

NPCHP24AB
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300446 
3
 
740
1
 
350
4
 
159
2
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
300446
99.5%
3 740
 
0.2%
1 350
 
0.1%
4 159
 
0.1%
2 128
 
< 0.1%

Length

2024-05-06T23:47:30.833892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:30.931136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 740
53.7%
1 350
25.4%
4 159
 
11.5%
2 128
 
9.3%

Most occurring characters

ValueCountFrequency (%)
300446
99.5%
3 740
 
0.2%
1 350
 
0.1%
4 159
 
0.1%
2 128
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300446
99.5%
Decimal Number 1377
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 740
53.7%
1 350
25.4%
4 159
 
11.5%
2 128
 
9.3%
Space Separator
ValueCountFrequency (%)
300446
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300446
99.5%
3 740
 
0.2%
1 350
 
0.1%
4 159
 
0.1%
2 128
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300446
99.5%
3 740
 
0.2%
1 350
 
0.1%
4 159
 
0.1%
2 128
 
< 0.1%

NPCHP24_1B
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301235 
1
 
588

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301235
99.8%
1 588
 
0.2%

Length

2024-05-06T23:47:31.036890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:31.131277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 588
100.0%

Most occurring characters

ValueCountFrequency (%)
301235
99.8%
1 588
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301235
99.8%
Decimal Number 588
 
0.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301235
100.0%
Decimal Number
ValueCountFrequency (%)
1 588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301235
99.8%
1 588
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301235
99.8%
1 588
 
0.2%

NPCHP24BA
Text

Distinct123
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:31.283168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.0102212
Min length1

Characters and Unicode

Total characters304908
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
6000000 46
 
7.8%
2000000 31
 
5.3%
1000000 31
 
5.3%
99 30
 
5.1%
300000 26
 
4.4%
4000000 24
 
4.1%
500000 22
 
3.7%
3000000 22
 
3.7%
1500000 17
 
2.9%
100000 15
 
2.6%
Other values (112) 324
55.1%
2024-05-06T23:47:31.606599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
301235
98.8%
0 2799
 
0.9%
1 158
 
0.1%
5 146
 
< 0.1%
2 132
 
< 0.1%
3 97
 
< 0.1%
6 86
 
< 0.1%
4 85
 
< 0.1%
9 82
 
< 0.1%
8 51
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301235
98.8%
Decimal Number 3673
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2799
76.2%
1 158
 
4.3%
5 146
 
4.0%
2 132
 
3.6%
3 97
 
2.6%
6 86
 
2.3%
4 85
 
2.3%
9 82
 
2.2%
8 51
 
1.4%
7 37
 
1.0%
Space Separator
ValueCountFrequency (%)
301235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301235
98.8%
0 2799
 
0.9%
1 158
 
0.1%
5 146
 
< 0.1%
2 132
 
< 0.1%
3 97
 
< 0.1%
6 86
 
< 0.1%
4 85
 
< 0.1%
9 82
 
< 0.1%
8 51
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301235
98.8%
0 2799
 
0.9%
1 158
 
0.1%
5 146
 
< 0.1%
2 132
 
< 0.1%
3 97
 
< 0.1%
6 86
 
< 0.1%
4 85
 
< 0.1%
9 82
 
< 0.1%
8 51
 
< 0.1%

NPCHP24BB
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301235 
3
 
328
1
 
121
4
 
116
2
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301235
99.8%
3 328
 
0.1%
1 121
 
< 0.1%
4 116
 
< 0.1%
2 23
 
< 0.1%

Length

2024-05-06T23:47:31.763322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:31.870700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 328
55.8%
1 121
 
20.6%
4 116
 
19.7%
2 23
 
3.9%

Most occurring characters

ValueCountFrequency (%)
301235
99.8%
3 328
 
0.1%
1 121
 
< 0.1%
4 116
 
< 0.1%
2 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301235
99.8%
Decimal Number 588
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 328
55.8%
1 121
 
20.6%
4 116
 
19.7%
2 23
 
3.9%
Space Separator
ValueCountFrequency (%)
301235
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301235
99.8%
3 328
 
0.1%
1 121
 
< 0.1%
4 116
 
< 0.1%
2 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301235
99.8%
3 328
 
0.1%
1 121
 
< 0.1%
4 116
 
< 0.1%
2 23
 
< 0.1%

NPCHP25A
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301080 
1
 
743

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301080
99.8%
1 743
 
0.2%

Length

2024-05-06T23:47:31.980490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:32.068977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 743
100.0%

Most occurring characters

ValueCountFrequency (%)
301080
99.8%
1 743
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301080
99.8%
Decimal Number 743
 
0.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301080
100.0%
Decimal Number
ValueCountFrequency (%)
1 743
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301080
99.8%
1 743
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301080
99.8%
1 743
 
0.2%

NPCHP25B
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301447 
1
 
376

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301447
99.9%
1 376
 
0.1%

Length

2024-05-06T23:47:32.164084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:32.254331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 376
100.0%

Most occurring characters

ValueCountFrequency (%)
301447
99.9%
1 376
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301447
99.9%
Decimal Number 376
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301447
100.0%
Decimal Number
ValueCountFrequency (%)
1 376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301447
99.9%
1 376
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301447
99.9%
1 376
 
0.1%

NPCHP25C
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301744 
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301744
> 99.9%
1 79
 
< 0.1%

Length

2024-05-06T23:47:32.352740image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:32.446013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 79
100.0%

Most occurring characters

ValueCountFrequency (%)
301744
> 99.9%
1 79
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301744
> 99.9%
Decimal Number 79
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301744
100.0%
Decimal Number
ValueCountFrequency (%)
1 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301744
> 99.9%
1 79
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301744
> 99.9%
1 79
 
< 0.1%

NPCHP25D
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301619 
1
 
204

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301619
99.9%
1 204
 
0.1%

Length

2024-05-06T23:47:32.545864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:32.639158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 204
100.0%

Most occurring characters

ValueCountFrequency (%)
301619
99.9%
1 204
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301619
99.9%
Decimal Number 204
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301619
100.0%
Decimal Number
ValueCountFrequency (%)
1 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301619
99.9%
1 204
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301619
99.9%
1 204
 
0.1%

NPCHP25E
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301712 
1
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301712
> 99.9%
1 111
 
< 0.1%

Length

2024-05-06T23:47:32.738473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:32.828446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 111
100.0%

Most occurring characters

ValueCountFrequency (%)
301712
> 99.9%
1 111
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301712
> 99.9%
Decimal Number 111
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301712
100.0%
Decimal Number
ValueCountFrequency (%)
1 111
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301712
> 99.9%
1 111
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301712
> 99.9%
1 111
 
< 0.1%

NPCHP25F
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301756 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301756
> 99.9%
1 67
 
< 0.1%

Length

2024-05-06T23:47:32.925277image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:33.014586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 67
100.0%

Most occurring characters

ValueCountFrequency (%)
301756
> 99.9%
1 67
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301756
> 99.9%
Decimal Number 67
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301756
100.0%
Decimal Number
ValueCountFrequency (%)
1 67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301756
> 99.9%
1 67
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301756
> 99.9%
1 67
 
< 0.1%

NPCHP25G
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301703 
1
 
120

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301703
> 99.9%
1 120
 
< 0.1%

Length

2024-05-06T23:47:33.119761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:33.215204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 120
100.0%

Most occurring characters

ValueCountFrequency (%)
301703
> 99.9%
1 120
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301703
> 99.9%
Decimal Number 120
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301703
100.0%
Decimal Number
ValueCountFrequency (%)
1 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301703
> 99.9%
1 120
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301703
> 99.9%
1 120
 
< 0.1%

NPCHP25H
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301628 
1
 
195

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301628
99.9%
1 195
 
0.1%

Length

2024-05-06T23:47:33.314023image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:33.408722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 195
100.0%

Most occurring characters

ValueCountFrequency (%)
301628
99.9%
1 195
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301628
99.9%
Decimal Number 195
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301628
100.0%
Decimal Number
ValueCountFrequency (%)
1 195
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301628
99.9%
1 195
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301628
99.9%
1 195
 
0.1%

NPCHP25I
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301696 
1
 
127

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301696
> 99.9%
1 127
 
< 0.1%

Length

2024-05-06T23:47:33.507684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:33.597879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 127
100.0%

Most occurring characters

ValueCountFrequency (%)
301696
> 99.9%
1 127
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301696
> 99.9%
Decimal Number 127
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301696
100.0%
Decimal Number
ValueCountFrequency (%)
1 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301696
> 99.9%
1 127
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301696
> 99.9%
1 127
 
< 0.1%

NPCHP28
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
2
78504 
1
 
2230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row2
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
2 78504
 
26.0%
1 2230
 
0.7%

Length

2024-05-06T23:47:33.694155image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:33.786518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 78504
97.2%
1 2230
 
2.8%

Most occurring characters

ValueCountFrequency (%)
221089
73.3%
2 78504
 
26.0%
1 2230
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
73.3%
Decimal Number 80734
 
26.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 78504
97.2%
1 2230
 
2.8%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
73.3%
2 78504
 
26.0%
1 2230
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
73.3%
2 78504
 
26.0%
1 2230
 
0.7%

NPCHP28A
Text

Distinct313
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:33.987474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length8
Median length1
Mean length1.042399
Min length1

Characters and Unicode

Total characters314620
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
3000000 108
 
4.8%
4000000 96
 
4.3%
5000000 92
 
4.1%
2000000 79
 
3.5%
6000000 72
 
3.2%
400000 57
 
2.6%
1000000 56
 
2.5%
500000 55
 
2.5%
1500000 54
 
2.4%
2500000 51
 
2.3%
Other values (302) 1510
67.7%
2024-05-06T23:47:34.373988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
299593
95.2%
0 11351
 
3.6%
5 661
 
0.2%
1 626
 
0.2%
2 535
 
0.2%
3 461
 
0.1%
4 431
 
0.1%
6 293
 
0.1%
8 277
 
0.1%
7 214
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299593
95.2%
Decimal Number 15027
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11351
75.5%
5 661
 
4.4%
1 626
 
4.2%
2 535
 
3.6%
3 461
 
3.1%
4 431
 
2.9%
6 293
 
1.9%
8 277
 
1.8%
7 214
 
1.4%
9 178
 
1.2%
Space Separator
ValueCountFrequency (%)
299593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 314620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299593
95.2%
0 11351
 
3.6%
5 661
 
0.2%
1 626
 
0.2%
2 535
 
0.2%
3 461
 
0.1%
4 431
 
0.1%
6 293
 
0.1%
8 277
 
0.1%
7 214
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 314620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299593
95.2%
0 11351
 
3.6%
5 661
 
0.2%
1 626
 
0.2%
2 535
 
0.2%
3 461
 
0.1%
4 431
 
0.1%
6 293
 
0.1%
8 277
 
0.1%
7 214
 
0.1%

NPCHP28B
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
299593 
3
 
1140
1
 
827
4
 
221
2
 
42

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
299593
99.3%
3 1140
 
0.4%
1 827
 
0.3%
4 221
 
0.1%
2 42
 
< 0.1%

Length

2024-05-06T23:47:34.536481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:34.636595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 1140
51.1%
1 827
37.1%
4 221
 
9.9%
2 42
 
1.9%

Most occurring characters

ValueCountFrequency (%)
299593
99.3%
3 1140
 
0.4%
1 827
 
0.3%
4 221
 
0.1%
2 42
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 299593
99.3%
Decimal Number 2230
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1140
51.1%
1 827
37.1%
4 221
 
9.9%
2 42
 
1.9%
Space Separator
ValueCountFrequency (%)
299593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
299593
99.3%
3 1140
 
0.4%
1 827
 
0.3%
4 221
 
0.1%
2 42
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
299593
99.3%
3 1140
 
0.4%
1 827
 
0.3%
4 221
 
0.1%
2 42
 
< 0.1%

NPCHP29A
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301443 
1
 
380

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301443
99.9%
1 380
 
0.1%

Length

2024-05-06T23:47:34.745110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:34.841390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 380
100.0%

Most occurring characters

ValueCountFrequency (%)
301443
99.9%
1 380
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301443
99.9%
Decimal Number 380
 
0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301443
100.0%
Decimal Number
ValueCountFrequency (%)
1 380
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301443
99.9%
1 380
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301443
99.9%
1 380
 
0.1%

NPCHP29B
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300780 
1
 
1043

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
300780
99.7%
1 1043
 
0.3%

Length

2024-05-06T23:47:34.942248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.046287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1043
100.0%

Most occurring characters

ValueCountFrequency (%)
300780
99.7%
1 1043
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300780
99.7%
Decimal Number 1043
 
0.3%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
300780
100.0%
Decimal Number
ValueCountFrequency (%)
1 1043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300780
99.7%
1 1043
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300780
99.7%
1 1043
 
0.3%

NPCHP29C
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301202 
1
 
621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301202
99.8%
1 621
 
0.2%

Length

2024-05-06T23:47:35.146122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.241060image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 621
100.0%

Most occurring characters

ValueCountFrequency (%)
301202
99.8%
1 621
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301202
99.8%
Decimal Number 621
 
0.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301202
100.0%
Decimal Number
ValueCountFrequency (%)
1 621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301202
99.8%
1 621
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301202
99.8%
1 621
 
0.2%

NPCHP29D
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301785 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301785
> 99.9%
1 38
 
< 0.1%

Length

2024-05-06T23:47:35.338251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.429149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 38
100.0%

Most occurring characters

ValueCountFrequency (%)
301785
> 99.9%
1 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301785
> 99.9%
Decimal Number 38
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301785
100.0%
Decimal Number
ValueCountFrequency (%)
1 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301785
> 99.9%
1 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301785
> 99.9%
1 38
 
< 0.1%

NPCHP29E
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301794 
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301794
> 99.9%
1 29
 
< 0.1%

Length

2024-05-06T23:47:35.526813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.617375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 29
100.0%

Most occurring characters

ValueCountFrequency (%)
301794
> 99.9%
1 29
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301794
> 99.9%
Decimal Number 29
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301794
100.0%
Decimal Number
ValueCountFrequency (%)
1 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301794
> 99.9%
1 29
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301794
> 99.9%
1 29
 
< 0.1%

NPCHP29F
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301798 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301798
> 99.9%
1 25
 
< 0.1%

Length

2024-05-06T23:47:35.715074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.806637image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 25
100.0%

Most occurring characters

ValueCountFrequency (%)
301798
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301798
> 99.9%
Decimal Number 25
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301798
100.0%
Decimal Number
ValueCountFrequency (%)
1 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301798
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301798
> 99.9%
1 25
 
< 0.1%

NPCHP29G
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301730 
1
 
93

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301730
> 99.9%
1 93
 
< 0.1%

Length

2024-05-06T23:47:35.902759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:35.992844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 93
100.0%

Most occurring characters

ValueCountFrequency (%)
301730
> 99.9%
1 93
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301730
> 99.9%
Decimal Number 93
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301730
100.0%
Decimal Number
ValueCountFrequency (%)
1 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301730
> 99.9%
1 93
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301730
> 99.9%
1 93
 
< 0.1%

NPCHP29H
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301799 
1
 
24

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301799
> 99.9%
1 24
 
< 0.1%

Length

2024-05-06T23:47:36.092118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:36.185792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 24
100.0%

Most occurring characters

ValueCountFrequency (%)
301799
> 99.9%
1 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301799
> 99.9%
Decimal Number 24
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301799
100.0%
Decimal Number
ValueCountFrequency (%)
1 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301799
> 99.9%
1 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301799
> 99.9%
1 24
 
< 0.1%

NPCHP29I
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
301788 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
301788
> 99.9%
1 35
 
< 0.1%

Length

2024-05-06T23:47:36.286390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:36.380859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 35
100.0%

Most occurring characters

ValueCountFrequency (%)
301788
> 99.9%
1 35
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 301788
> 99.9%
Decimal Number 35
 
< 0.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
301788
100.0%
Decimal Number
ValueCountFrequency (%)
1 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
301788
> 99.9%
1 35
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301788
> 99.9%
1 35
 
< 0.1%

NPCHP30A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
264364 
1
37459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row1
4th row
5th row1

Common Values

ValueCountFrequency (%)
264364
87.6%
1 37459
 
12.4%

Length

2024-05-06T23:47:36.488220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:36.582130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 37459
100.0%

Most occurring characters

ValueCountFrequency (%)
264364
87.6%
1 37459
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 264364
87.6%
Decimal Number 37459
 
12.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
264364
100.0%
Decimal Number
ValueCountFrequency (%)
1 37459
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
264364
87.6%
1 37459
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264364
87.6%
1 37459
 
12.4%

NPCHP30B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
263224 
1
38599 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row1
4th row1
5th row

Common Values

ValueCountFrequency (%)
263224
87.2%
1 38599
 
12.8%

Length

2024-05-06T23:47:36.683307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:36.776888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 38599
100.0%

Most occurring characters

ValueCountFrequency (%)
263224
87.2%
1 38599
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Space Separator 263224
87.2%
Decimal Number 38599
 
12.8%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
263224
100.0%
Decimal Number
ValueCountFrequency (%)
1 38599
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
263224
87.2%
1 38599
 
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
263224
87.2%
1 38599
 
12.8%

NPCHP30C
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
278906 
1
 
22917

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
278906
92.4%
1 22917
 
7.6%

Length

2024-05-06T23:47:36.880844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:36.974995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 22917
100.0%

Most occurring characters

ValueCountFrequency (%)
278906
92.4%
1 22917
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 278906
92.4%
Decimal Number 22917
 
7.6%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
278906
100.0%
Decimal Number
ValueCountFrequency (%)
1 22917
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
278906
92.4%
1 22917
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
278906
92.4%
1 22917
 
7.6%

NPCHP30D
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
289383 
1
 
12440

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
289383
95.9%
1 12440
 
4.1%

Length

2024-05-06T23:47:37.532290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:37.621981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 12440
100.0%

Most occurring characters

ValueCountFrequency (%)
289383
95.9%
1 12440
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 289383
95.9%
Decimal Number 12440
 
4.1%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
289383
100.0%
Decimal Number
ValueCountFrequency (%)
1 12440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
289383
95.9%
1 12440
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
289383
95.9%
1 12440
 
4.1%

NPCHP30E
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
284659 
1
 
17164

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
284659
94.3%
1 17164
 
5.7%

Length

2024-05-06T23:47:37.717585image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:37.809318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 17164
100.0%

Most occurring characters

ValueCountFrequency (%)
284659
94.3%
1 17164
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 284659
94.3%
Decimal Number 17164
 
5.7%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
284659
100.0%
Decimal Number
ValueCountFrequency (%)
1 17164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
284659
94.3%
1 17164
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
284659
94.3%
1 17164
 
5.7%

NPCHP30F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
240717 
1
61106 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
240717
79.8%
1 61106
 
20.2%

Length

2024-05-06T23:47:37.906693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:37.996400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 61106
100.0%

Most occurring characters

ValueCountFrequency (%)
240717
79.8%
1 61106
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 240717
79.8%
Decimal Number 61106
 
20.2%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
240717
100.0%
Decimal Number
ValueCountFrequency (%)
1 61106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
240717
79.8%
1 61106
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
240717
79.8%
1 61106
 
20.2%

NPCHP30G
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
239261 
1
62562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row1
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
239261
79.3%
1 62562
 
20.7%

Length

2024-05-06T23:47:38.092089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:38.185420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 62562
100.0%

Most occurring characters

ValueCountFrequency (%)
239261
79.3%
1 62562
 
20.7%

Most occurring categories

ValueCountFrequency (%)
Space Separator 239261
79.3%
Decimal Number 62562
 
20.7%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
239261
100.0%
Decimal Number
ValueCountFrequency (%)
1 62562
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
239261
79.3%
1 62562
 
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
239261
79.3%
1 62562
 
20.7%

NPCHP30H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
264316 
1
37507 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
264316
87.6%
1 37507
 
12.4%

Length

2024-05-06T23:47:38.285056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:38.377449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 37507
100.0%

Most occurring characters

ValueCountFrequency (%)
264316
87.6%
1 37507
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 264316
87.6%
Decimal Number 37507
 
12.4%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
264316
100.0%
Decimal Number
ValueCountFrequency (%)
1 37507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
264316
87.6%
1 37507
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264316
87.6%
1 37507
 
12.4%

NPCHP30I
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
182161 
1
119662 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row

Common Values

ValueCountFrequency (%)
182161
60.4%
1 119662
39.6%

Length

2024-05-06T23:47:38.475677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:38.570400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 119662
100.0%

Most occurring characters

ValueCountFrequency (%)
182161
60.4%
1 119662
39.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 182161
60.4%
Decimal Number 119662
39.6%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
182161
100.0%
Decimal Number
ValueCountFrequency (%)
1 119662
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
182161
60.4%
1 119662
39.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
182161
60.4%
1 119662
39.6%

NPCHP30J
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
223748 
1
78075 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row
5th row1

Common Values

ValueCountFrequency (%)
223748
74.1%
1 78075
 
25.9%

Length

2024-05-06T23:47:38.673073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:38.765574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 78075
100.0%

Most occurring characters

ValueCountFrequency (%)
223748
74.1%
1 78075
 
25.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 223748
74.1%
Decimal Number 78075
 
25.9%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
223748
100.0%
Decimal Number
ValueCountFrequency (%)
1 78075
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
223748
74.1%
1 78075
 
25.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
223748
74.1%
1 78075
 
25.9%

NPCHP30K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
1
160333 
141490 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row1
3rd row1
4th row
5th row

Common Values

ValueCountFrequency (%)
1 160333
53.1%
141490
46.9%

Length

2024-05-06T23:47:38.867821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:38.965413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 160333
100.0%

Most occurring characters

ValueCountFrequency (%)
1 160333
53.1%
141490
46.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160333
53.1%
Space Separator 141490
46.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 160333
100.0%
Space Separator
ValueCountFrequency (%)
141490
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 160333
53.1%
141490
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 160333
53.1%
141490
46.9%

NPCHP30L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
226581 
1
75242 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
226581
75.1%
1 75242
 
24.9%

Length

2024-05-06T23:47:39.071228image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:39.163924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 75242
100.0%

Most occurring characters

ValueCountFrequency (%)
226581
75.1%
1 75242
 
24.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 226581
75.1%
Decimal Number 75242
 
24.9%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
226581
100.0%
Decimal Number
ValueCountFrequency (%)
1 75242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
226581
75.1%
1 75242
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
226581
75.1%
1 75242
 
24.9%

NPCHP31AA
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6612551
Minimum0
Maximum120
Zeros61675
Zeros (%)20.4%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:39.275623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q310
95-th percentile25
Maximum120
Range120
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.0182196
Coefficient of variation (CV)1.0412139
Kurtosis8.6695801
Mean8.6612551
Median Absolute Deviation (MAD)4
Skewness2.1165907
Sum2614166
Variance81.328284
MonotonicityNot monotonic
2024-05-06T23:47:39.416410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61675
20.4%
10 54552
18.1%
5 45728
15.2%
15 23868
 
7.9%
20 18144
 
6.0%
3 12559
 
4.2%
2 12016
 
4.0%
4 11710
 
3.9%
6 9101
 
3.0%
8 8071
 
2.7%
Other values (90) 44399
14.7%
ValueCountFrequency (%)
0 61675
20.4%
1 6197
 
2.1%
2 12016
 
4.0%
3 12559
 
4.2%
4 11710
 
3.9%
5 45728
15.2%
6 9101
 
3.0%
7 3998
 
1.3%
8 8071
 
2.7%
9 1474
 
0.5%
ValueCountFrequency (%)
120 16
< 0.1%
119 1
 
< 0.1%
116 1
 
< 0.1%
115 1
 
< 0.1%
110 1
 
< 0.1%
108 2
 
< 0.1%
106 6
 
< 0.1%
105 1
 
< 0.1%
104 15
< 0.1%
103 1
 
< 0.1%

NPCHP31AB
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6876149
Minimum0
Maximum48
Zeros38118
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:39.545800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile12
Maximum48
Range48
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0443318
Coefficient of variation (CV)0.86276964
Kurtosis6.9421346
Mean4.6876149
Median Absolute Deviation (MAD)2
Skewness1.8608271
Sum1414830
Variance16.35662
MonotonicityNot monotonic
2024-05-06T23:47:39.683070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
4 56463
18.7%
2 52810
17.5%
0 38118
12.6%
6 31108
10.3%
5 22365
 
7.4%
3 22316
 
7.4%
10 20503
 
6.8%
8 20433
 
6.8%
1 12924
 
4.3%
12 6680
 
2.2%
Other values (37) 18103
 
6.0%
ValueCountFrequency (%)
0 38118
12.6%
1 12924
 
4.3%
2 52810
17.5%
3 22316
 
7.4%
4 56463
18.7%
5 22365
 
7.4%
6 31108
10.3%
7 4275
 
1.4%
8 20433
 
6.8%
9 1540
 
0.5%
ValueCountFrequency (%)
48 24
 
< 0.1%
47 2
 
< 0.1%
46 1
 
< 0.1%
45 11
 
< 0.1%
43 1
 
< 0.1%
42 4
 
< 0.1%
40 63
< 0.1%
39 4
 
< 0.1%
38 4
 
< 0.1%
37 5
 
< 0.1%

NPCHP31BA
Real number (ℝ)

ZEROS 

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6483767
Minimum0
Maximum120
Zeros157243
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:39.818189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile20
Maximum120
Range120
Interquartile range (IQR)5

Descriptive statistics

Standard deviation8.2445242
Coefficient of variation (CV)1.7736351
Kurtosis15.885631
Mean4.6483767
Median Absolute Deviation (MAD)0
Skewness3.204453
Sum1402987
Variance67.972179
MonotonicityNot monotonic
2024-05-06T23:47:39.966510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157243
52.1%
5 27802
 
9.2%
10 22304
 
7.4%
2 13285
 
4.4%
3 12875
 
4.3%
15 10238
 
3.4%
4 9836
 
3.3%
20 8820
 
2.9%
1 7863
 
2.6%
6 5896
 
2.0%
Other values (84) 25661
 
8.5%
ValueCountFrequency (%)
0 157243
52.1%
1 7863
 
2.6%
2 13285
 
4.4%
3 12875
 
4.3%
4 9836
 
3.3%
5 27802
 
9.2%
6 5896
 
2.0%
7 2320
 
0.8%
8 4445
 
1.5%
9 899
 
0.3%
ValueCountFrequency (%)
120 18
< 0.1%
116 1
 
< 0.1%
115 1
 
< 0.1%
112 1
 
< 0.1%
105 1
 
< 0.1%
104 2
 
< 0.1%
103 1
 
< 0.1%
102 4
 
< 0.1%
101 1
 
< 0.1%
100 20
< 0.1%

NPCHP31BB
Real number (ℝ)

ZEROS 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2145363
Minimum0
Maximum48
Zeros165070
Zeros (%)54.7%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:40.119048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile10
Maximum48
Range48
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.8200551
Coefficient of variation (CV)1.7249909
Kurtosis15.433306
Mean2.2145363
Median Absolute Deviation (MAD)0
Skewness3.0885937
Sum668398
Variance14.592821
MonotonicityNot monotonic
2024-05-06T23:47:40.258389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 165070
54.7%
2 33899
 
11.2%
4 21539
 
7.1%
1 16757
 
5.6%
3 13562
 
4.5%
6 11412
 
3.8%
5 10555
 
3.5%
10 9107
 
3.0%
8 7134
 
2.4%
12 2883
 
1.0%
Other values (37) 9905
 
3.3%
ValueCountFrequency (%)
0 165070
54.7%
1 16757
 
5.6%
2 33899
 
11.2%
3 13562
 
4.5%
4 21539
 
7.1%
5 10555
 
3.5%
6 11412
 
3.8%
7 1833
 
0.6%
8 7134
 
2.4%
9 866
 
0.3%
ValueCountFrequency (%)
48 55
< 0.1%
47 3
 
< 0.1%
46 1
 
< 0.1%
45 12
 
< 0.1%
42 3
 
< 0.1%
41 1
 
< 0.1%
40 51
< 0.1%
39 2
 
< 0.1%
38 17
 
< 0.1%
37 8
 
< 0.1%

NPCHP31CA
Real number (ℝ)

ZEROS 

Distinct90
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.36703
Minimum0
Maximum120
Zeros3244
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:40.392685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q135
median40
Q340
95-th percentile48
Maximum120
Range120
Interquartile range (IQR)5

Descriptive statistics

Standard deviation10.049735
Coefficient of variation (CV)0.27634193
Kurtosis5.5836177
Mean36.36703
Median Absolute Deviation (MAD)5
Skewness-1.18135
Sum10976406
Variance100.99717
MonotonicityNot monotonic
2024-05-06T23:47:40.544964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 138696
46.0%
35 39865
 
13.2%
30 34476
 
11.4%
48 13897
 
4.6%
45 13508
 
4.5%
25 9719
 
3.2%
50 8909
 
3.0%
8 5956
 
2.0%
20 4540
 
1.5%
36 4518
 
1.5%
Other values (80) 27739
 
9.2%
ValueCountFrequency (%)
0 3244
1.1%
1 96
 
< 0.1%
2 383
 
0.1%
3 497
 
0.2%
4 571
 
0.2%
5 987
 
0.3%
6 1646
 
0.5%
7 1765
 
0.6%
8 5956
2.0%
9 505
 
0.2%
ValueCountFrequency (%)
120 143
< 0.1%
110 9
 
< 0.1%
104 1
 
< 0.1%
100 16
 
< 0.1%
96 3
 
< 0.1%
95 3
 
< 0.1%
90 10
 
< 0.1%
88 2
 
< 0.1%
84 6
 
< 0.1%
82 1
 
< 0.1%

NPCHP31CB
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.945899
Minimum0
Maximum48
Zeros3546
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:40.688713image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q114
median16
Q316
95-th percentile20
Maximum48
Range48
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.3378692
Coefficient of variation (CV)0.2902381
Kurtosis9.6627519
Mean14.945899
Median Absolute Deviation (MAD)2
Skewness0.61155619
Sum4511016
Variance18.817109
MonotonicityNot monotonic
2024-05-06T23:47:40.833195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
16 132608
43.9%
14 32767
 
10.9%
12 29517
 
9.8%
18 21267
 
7.0%
20 21078
 
7.0%
10 14628
 
4.8%
8 9655
 
3.2%
15 8504
 
2.8%
17 4598
 
1.5%
0 3546
 
1.2%
Other values (39) 23655
 
7.8%
ValueCountFrequency (%)
0 3546
 
1.2%
1 321
 
0.1%
2 892
 
0.3%
3 704
 
0.2%
4 1386
 
0.5%
5 1371
 
0.5%
6 2960
 
1.0%
7 2188
 
0.7%
8 9655
3.2%
9 1786
 
0.6%
ValueCountFrequency (%)
48 262
 
0.1%
47 13
 
< 0.1%
46 28
 
< 0.1%
45 105
 
< 0.1%
44 10
 
< 0.1%
43 10
 
< 0.1%
42 58
 
< 0.1%
41 3
 
< 0.1%
40 981
0.3%
39 10
 
< 0.1%

NPCHP31DA
Text

Distinct77
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:40.960186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length1
Mean length1.0367566
Min length1

Characters and Unicode

Total characters312917
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row6
5th row
ValueCountFrequency (%)
0 49772
61.6%
5 5764
 
7.1%
10 4666
 
5.8%
2 3528
 
4.4%
3 2943
 
3.6%
4 2930
 
3.6%
15 1746
 
2.2%
6 1552
 
1.9%
1 1497
 
1.9%
20 1253
 
1.6%
Other values (66) 5083
 
6.3%
2024-05-06T23:47:41.232191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
221089
70.7%
0 57096
 
18.2%
1 8851
 
2.8%
5 8365
 
2.7%
2 5913
 
1.9%
3 3979
 
1.3%
4 3882
 
1.2%
6 1772
 
0.6%
8 1163
 
0.4%
7 570
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
70.7%
Decimal Number 91828
29.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57096
62.2%
1 8851
 
9.6%
5 8365
 
9.1%
2 5913
 
6.4%
3 3979
 
4.3%
4 3882
 
4.2%
6 1772
 
1.9%
8 1163
 
1.3%
7 570
 
0.6%
9 237
 
0.3%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312917
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
70.7%
0 57096
 
18.2%
1 8851
 
2.8%
5 8365
 
2.7%
2 5913
 
1.9%
3 3979
 
1.3%
4 3882
 
1.2%
6 1772
 
0.6%
8 1163
 
0.4%
7 570
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
70.7%
0 57096
 
18.2%
1 8851
 
2.8%
5 8365
 
2.7%
2 5913
 
1.9%
3 3979
 
1.3%
4 3882
 
1.2%
6 1772
 
0.6%
8 1163
 
0.4%
7 570
 
0.2%

NPCHP31DB
Categorical

IMBALANCE 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
221089 
0
57320 
2
 
7017
4
 
4243
1
 
3157
Other values (38)
 
8997

Length

Max length2
Median length1
Mean length1.0052912
Min length1

Characters and Unicode

Total characters303420
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row4
5th row

Common Values

ValueCountFrequency (%)
221089
73.3%
0 57320
 
19.0%
2 7017
 
2.3%
4 4243
 
1.4%
1 3157
 
1.0%
3 2882
 
1.0%
6 1605
 
0.5%
5 1575
 
0.5%
8 928
 
0.3%
10 760
 
0.3%
Other values (33) 1247
 
0.4%

Length

2024-05-06T23:47:41.372842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 57320
71.0%
2 7017
 
8.7%
4 4243
 
5.3%
1 3157
 
3.9%
3 2882
 
3.6%
6 1605
 
2.0%
5 1575
 
2.0%
8 928
 
1.1%
10 760
 
0.9%
12 277
 
0.3%
Other values (32) 970
 
1.2%

Most occurring characters

ValueCountFrequency (%)
221089
72.9%
0 58203
 
19.2%
2 7471
 
2.5%
1 4568
 
1.5%
4 4330
 
1.4%
3 2939
 
1.0%
6 1755
 
0.6%
5 1674
 
0.6%
8 953
 
0.3%
7 296
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 221089
72.9%
Decimal Number 82331
 
27.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58203
70.7%
2 7471
 
9.1%
1 4568
 
5.5%
4 4330
 
5.3%
3 2939
 
3.6%
6 1755
 
2.1%
5 1674
 
2.0%
8 953
 
1.2%
7 296
 
0.4%
9 142
 
0.2%
Space Separator
ValueCountFrequency (%)
221089
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 303420
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
221089
72.9%
0 58203
 
19.2%
2 7471
 
2.5%
1 4568
 
1.5%
4 4330
 
1.4%
3 2939
 
1.0%
6 1755
 
0.6%
5 1674
 
0.6%
8 953
 
0.3%
7 296
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 303420
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
221089
72.9%
0 58203
 
19.2%
2 7471
 
2.5%
1 4568
 
1.5%
4 4330
 
1.4%
3 2939
 
1.0%
6 1755
 
0.6%
5 1674
 
0.6%
8 953
 
0.3%
7 296
 
0.1%

NPCHP31EA
Real number (ℝ)

ZEROS 

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9218681
Minimum0
Maximum120
Zeros188101
Zeros (%)62.3%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:41.494136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile10
Maximum120
Range120
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.1508382
Coefficient of variation (CV)2.1597935
Kurtosis97.6333
Mean1.9218681
Median Absolute Deviation (MAD)0
Skewness6.6800467
Sum580064
Variance17.229458
MonotonicityNot monotonic
2024-05-06T23:47:41.631138image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 188101
62.3%
2 23054
 
7.6%
5 19602
 
6.5%
3 17328
 
5.7%
4 14680
 
4.9%
1 10993
 
3.6%
10 9017
 
3.0%
6 6752
 
2.2%
8 3050
 
1.0%
15 2084
 
0.7%
Other values (71) 7162
 
2.4%
ValueCountFrequency (%)
0 188101
62.3%
1 10993
 
3.6%
2 23054
 
7.6%
3 17328
 
5.7%
4 14680
 
4.9%
5 19602
 
6.5%
6 6752
 
2.2%
7 1561
 
0.5%
8 3050
 
1.0%
9 813
 
0.3%
ValueCountFrequency (%)
120 20
< 0.1%
110 1
 
< 0.1%
104 1
 
< 0.1%
100 6
 
< 0.1%
99 3
 
< 0.1%
90 9
< 0.1%
88 1
 
< 0.1%
82 1
 
< 0.1%
80 3
 
< 0.1%
79 1
 
< 0.1%

NPCHP31EB
Real number (ℝ)

ZEROS 

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7397183
Minimum0
Maximum120
Zeros168356
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:41.770206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile20
Maximum120
Range120
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.4111045
Coefficient of variation (CV)1.981728
Kurtosis20.312515
Mean3.7397183
Median Absolute Deviation (MAD)0
Skewness3.6603344
Sum1128733
Variance54.92447
MonotonicityNot monotonic
2024-05-06T23:47:41.915894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 168356
55.8%
5 24232
 
8.0%
10 18479
 
6.1%
1 17838
 
5.9%
2 12436
 
4.1%
3 11599
 
3.8%
4 9349
 
3.1%
15 7707
 
2.6%
20 6247
 
2.1%
6 5324
 
1.8%
Other values (81) 20256
 
6.7%
ValueCountFrequency (%)
0 168356
55.8%
1 17838
 
5.9%
2 12436
 
4.1%
3 11599
 
3.8%
4 9349
 
3.1%
5 24232
 
8.0%
6 5324
 
1.8%
7 1981
 
0.7%
8 3746
 
1.2%
9 806
 
0.3%
ValueCountFrequency (%)
120 13
< 0.1%
112 1
 
< 0.1%
105 1
 
< 0.1%
104 2
 
< 0.1%
103 1
 
< 0.1%
102 3
 
< 0.1%
101 1
 
< 0.1%
100 14
< 0.1%
99 4
 
< 0.1%
95 1
 
< 0.1%

NPCHP31FA
Real number (ℝ)

ZEROS 

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4519603
Minimum0
Maximum120
Zeros159691
Zeros (%)52.9%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:42.057841image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile10
Maximum120
Range120
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.6829947
Coefficient of variation (CV)1.9098983
Kurtosis53.797716
Mean2.4519603
Median Absolute Deviation (MAD)0
Skewness5.1710995
Sum740058
Variance21.93044
MonotonicityNot monotonic
2024-05-06T23:47:42.199033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159691
52.9%
5 29903
 
9.9%
2 25336
 
8.4%
3 21035
 
7.0%
1 19927
 
6.6%
4 13573
 
4.5%
10 11539
 
3.8%
6 5078
 
1.7%
15 3515
 
1.2%
8 2719
 
0.9%
Other values (72) 9507
 
3.1%
ValueCountFrequency (%)
0 159691
52.9%
1 19927
 
6.6%
2 25336
 
8.4%
3 21035
 
7.0%
4 13573
 
4.5%
5 29903
 
9.9%
6 5078
 
1.7%
7 1823
 
0.6%
8 2719
 
0.9%
9 530
 
0.2%
ValueCountFrequency (%)
120 8
< 0.1%
110 1
 
< 0.1%
105 1
 
< 0.1%
103 1
 
< 0.1%
102 5
< 0.1%
101 2
 
< 0.1%
100 4
< 0.1%
99 5
< 0.1%
98 1
 
< 0.1%
96 1
 
< 0.1%

NPCHP31FB
Real number (ℝ)

ZEROS 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.95357875
Minimum0
Maximum48
Zeros198734
Zeros (%)65.8%
Negative0
Negative (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:42.335997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum48
Range48
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0462742
Coefficient of variation (CV)2.1458891
Kurtosis54.872788
Mean0.95357875
Median Absolute Deviation (MAD)0
Skewness5.2648516
Sum287812
Variance4.1872381
MonotonicityNot monotonic
2024-05-06T23:47:42.471466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 198734
65.8%
2 36690
 
12.2%
1 31178
 
10.3%
4 11506
 
3.8%
3 10002
 
3.3%
5 4080
 
1.4%
6 3595
 
1.2%
8 1783
 
0.6%
10 1609
 
0.5%
7 669
 
0.2%
Other values (38) 1977
 
0.7%
ValueCountFrequency (%)
0 198734
65.8%
1 31178
 
10.3%
2 36690
 
12.2%
3 10002
 
3.3%
4 11506
 
3.8%
5 4080
 
1.4%
6 3595
 
1.2%
7 669
 
0.2%
8 1783
 
0.6%
9 342
 
0.1%
ValueCountFrequency (%)
48 5
 
< 0.1%
47 4
 
< 0.1%
46 1
 
< 0.1%
45 2
 
< 0.1%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 4
 
< 0.1%
41 1
 
< 0.1%
40 15
< 0.1%
38 2
 
< 0.1%

NPCHP32
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
244678 
1
45109 
4
 
6563
2
 
5147
3
 
326

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
244678
81.1%
1 45109
 
14.9%
4 6563
 
2.2%
2 5147
 
1.7%
3 326
 
0.1%

Length

2024-05-06T23:47:42.590781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:42.691814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
1 45109
78.9%
4 6563
 
11.5%
2 5147
 
9.0%
3 326
 
0.6%

Most occurring characters

ValueCountFrequency (%)
244678
81.1%
1 45109
 
14.9%
4 6563
 
2.2%
2 5147
 
1.7%
3 326
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 244678
81.1%
Decimal Number 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45109
78.9%
4 6563
 
11.5%
2 5147
 
9.0%
3 326
 
0.6%
Space Separator
ValueCountFrequency (%)
244678
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
244678
81.1%
1 45109
 
14.9%
4 6563
 
2.2%
2 5147
 
1.7%
3 326
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244678
81.1%
1 45109
 
14.9%
4 6563
 
2.2%
2 5147
 
1.7%
3 326
 
0.1%

NPCHP32A
Categorical

IMBALANCE 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
256714 
2
 
22079
1
 
16653
3
 
3012
4
 
1763
Other values (10)
 
1602

Length

Max length2
Median length1
Mean length1.0000563
Min length1

Characters and Unicode

Total characters301840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
256714
85.1%
2 22079
 
7.3%
1 16653
 
5.5%
3 3012
 
1.0%
4 1763
 
0.6%
5 953
 
0.3%
6 435
 
0.1%
7 133
 
< 0.1%
8 45
 
< 0.1%
9 19
 
< 0.1%
Other values (5) 17
 
< 0.1%

Length

2024-05-06T23:47:42.806062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 22079
48.9%
1 16653
36.9%
3 3012
 
6.7%
4 1763
 
3.9%
5 953
 
2.1%
6 435
 
1.0%
7 133
 
0.3%
8 45
 
0.1%
9 19
 
< 0.1%
10 10
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
256714
85.0%
2 22080
 
7.3%
1 16674
 
5.5%
3 3013
 
1.0%
4 1764
 
0.6%
5 953
 
0.3%
6 435
 
0.1%
7 133
 
< 0.1%
8 45
 
< 0.1%
9 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 256714
85.0%
Decimal Number 45126
 
15.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22080
48.9%
1 16674
36.9%
3 3013
 
6.7%
4 1764
 
3.9%
5 953
 
2.1%
6 435
 
1.0%
7 133
 
0.3%
8 45
 
0.1%
9 19
 
< 0.1%
0 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
256714
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
256714
85.0%
2 22080
 
7.3%
1 16674
 
5.5%
3 3013
 
1.0%
4 1764
 
0.6%
5 953
 
0.3%
6 435
 
0.1%
7 133
 
< 0.1%
8 45
 
< 0.1%
9 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
256714
85.0%
2 22080
 
7.3%
1 16674
 
5.5%
3 3013
 
1.0%
4 1764
 
0.6%
5 953
 
0.3%
6 435
 
0.1%
7 133
 
< 0.1%
8 45
 
< 0.1%
9 19
 
< 0.1%

NPCHP32B
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
296676 
2
 
4850
1
 
297

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
296676
98.3%
2 4850
 
1.6%
1 297
 
0.1%

Length

2024-05-06T23:47:42.915681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:43.010082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 4850
94.2%
1 297
 
5.8%

Most occurring characters

ValueCountFrequency (%)
296676
98.3%
2 4850
 
1.6%
1 297
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 296676
98.3%
Decimal Number 5147
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4850
94.2%
1 297
 
5.8%
Space Separator
ValueCountFrequency (%)
296676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
296676
98.3%
2 4850
 
1.6%
1 297
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296676
98.3%
2 4850
 
1.6%
1 297
 
0.1%

NPCHP33
Text

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:43.168055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.0170497
Min length1

Characters and Unicode

Total characters306969
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
50 359
 
7.0%
60 327
 
6.4%
45 256
 
5.0%
40 237
 
4.6%
55 203
 
3.9%
56 199
 
3.9%
30 189
 
3.7%
65 171
 
3.3%
35 146
 
2.8%
62 142
 
2.8%
Other values (73) 2918
56.7%
2024-05-06T23:47:43.488375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296676
96.6%
5 2366
 
0.8%
6 1555
 
0.5%
4 1266
 
0.4%
0 1260
 
0.4%
3 1049
 
0.3%
2 939
 
0.3%
7 686
 
0.2%
8 540
 
0.2%
1 354
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 296676
96.6%
Decimal Number 10293
 
3.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2366
23.0%
6 1555
15.1%
4 1266
12.3%
0 1260
12.2%
3 1049
10.2%
2 939
 
9.1%
7 686
 
6.7%
8 540
 
5.2%
1 354
 
3.4%
9 278
 
2.7%
Space Separator
ValueCountFrequency (%)
296676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 306969
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
296676
96.6%
5 2366
 
0.8%
6 1555
 
0.5%
4 1266
 
0.4%
0 1260
 
0.4%
3 1049
 
0.3%
2 939
 
0.3%
7 686
 
0.2%
8 540
 
0.2%
1 354
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306969
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296676
96.6%
5 2366
 
0.8%
6 1555
 
0.5%
4 1266
 
0.4%
0 1260
 
0.4%
3 1049
 
0.3%
2 939
 
0.3%
7 686
 
0.2%
8 540
 
0.2%
1 354
 
0.1%

NPCHP34
Categorical

IMBALANCE 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
296676 
2
 
1750
4
 
1671
1
 
477
3
 
364
Other values (7)
 
885

Length

Max length2
Median length1
Mean length1.0012557
Min length1

Characters and Unicode

Total characters302202
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
296676
98.3%
2 1750
 
0.6%
4 1671
 
0.6%
1 477
 
0.2%
3 364
 
0.1%
99 293
 
0.1%
8 203
 
0.1%
6 146
 
< 0.1%
5 87
 
< 0.1%
10 86
 
< 0.1%
Other values (2) 70
 
< 0.1%

Length

2024-05-06T23:47:43.646219image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 1750
34.0%
4 1671
32.5%
1 477
 
9.3%
3 364
 
7.1%
99 293
 
5.7%
8 203
 
3.9%
6 146
 
2.8%
5 87
 
1.7%
10 86
 
1.7%
7 42
 
0.8%

Most occurring characters

ValueCountFrequency (%)
296676
98.2%
2 1750
 
0.6%
4 1671
 
0.6%
9 614
 
0.2%
1 563
 
0.2%
3 364
 
0.1%
8 203
 
0.1%
6 146
 
< 0.1%
5 87
 
< 0.1%
0 86
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 296676
98.2%
Decimal Number 5526
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1750
31.7%
4 1671
30.2%
9 614
 
11.1%
1 563
 
10.2%
3 364
 
6.6%
8 203
 
3.7%
6 146
 
2.6%
5 87
 
1.6%
0 86
 
1.6%
7 42
 
0.8%
Space Separator
ValueCountFrequency (%)
296676
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 302202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
296676
98.2%
2 1750
 
0.6%
4 1671
 
0.6%
9 614
 
0.2%
1 563
 
0.2%
3 364
 
0.1%
8 203
 
0.1%
6 146
 
< 0.1%
5 87
 
< 0.1%
0 86
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 302202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296676
98.2%
2 1750
 
0.6%
4 1671
 
0.6%
9 614
 
0.2%
1 563
 
0.2%
3 364
 
0.1%
8 203
 
0.1%
6 146
 
< 0.1%
5 87
 
< 0.1%
0 86
 
< 0.1%

NPCHP34A
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
300825 
3
 
351
4
 
246
2
 
237
1
 
94
Other values (4)
 
70

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
300825
99.7%
3 351
 
0.1%
4 246
 
0.1%
2 237
 
0.1%
1 94
 
< 0.1%
5 64
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Length

2024-05-06T23:47:43.763271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:43.875755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 351
35.2%
4 246
24.6%
2 237
23.7%
1 94
 
9.4%
5 64
 
6.4%
7 4
 
0.4%
8 1
 
0.1%
6 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
300825
99.7%
3 351
 
0.1%
4 246
 
0.1%
2 237
 
0.1%
1 94
 
< 0.1%
5 64
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Space Separator 300825
99.7%
Decimal Number 998
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 351
35.2%
4 246
24.6%
2 237
23.7%
1 94
 
9.4%
5 64
 
6.4%
7 4
 
0.4%
8 1
 
0.1%
6 1
 
0.1%
Space Separator
ValueCountFrequency (%)
300825
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
300825
99.7%
3 351
 
0.1%
4 246
 
0.1%
2 237
 
0.1%
1 94
 
< 0.1%
5 64
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300825
99.7%
3 351
 
0.1%
4 246
 
0.1%
2 237
 
0.1%
1 94
 
< 0.1%
5 64
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
6 1
 
< 0.1%

NPCHP35A
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
227451 
57145 
1
 
17227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 227451
75.4%
57145
 
18.9%
1 17227
 
5.7%

Length

2024-05-06T23:47:43.991897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:44.085730image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 227451
93.0%
1 17227
 
7.0%

Most occurring characters

ValueCountFrequency (%)
2 227451
75.4%
57145
 
18.9%
1 17227
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 227451
93.0%
1 17227
 
7.0%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 227451
75.4%
57145
 
18.9%
1 17227
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 227451
75.4%
57145
 
18.9%
1 17227
 
5.7%

NPCHP35B
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
231211 
57145 
1
 
13467

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 231211
76.6%
57145
 
18.9%
1 13467
 
4.5%

Length

2024-05-06T23:47:44.185525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:44.277590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 231211
94.5%
1 13467
 
5.5%

Most occurring characters

ValueCountFrequency (%)
2 231211
76.6%
57145
 
18.9%
1 13467
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 231211
94.5%
1 13467
 
5.5%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 231211
76.6%
57145
 
18.9%
1 13467
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 231211
76.6%
57145
 
18.9%
1 13467
 
4.5%

NPCHP35C
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
220809 
57145 
1
23869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 220809
73.2%
57145
 
18.9%
1 23869
 
7.9%

Length

2024-05-06T23:47:44.380614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:44.484827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 220809
90.2%
1 23869
 
9.8%

Most occurring characters

ValueCountFrequency (%)
2 220809
73.2%
57145
 
18.9%
1 23869
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 220809
90.2%
1 23869
 
9.8%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 220809
73.2%
57145
 
18.9%
1 23869
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 220809
73.2%
57145
 
18.9%
1 23869
 
7.9%

NPCHP35D
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
227841 
57145 
1
 
16837

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 227841
75.5%
57145
 
18.9%
1 16837
 
5.6%

Length

2024-05-06T23:47:44.597513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:44.704201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 227841
93.1%
1 16837
 
6.9%

Most occurring characters

ValueCountFrequency (%)
2 227841
75.5%
57145
 
18.9%
1 16837
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 227841
93.1%
1 16837
 
6.9%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 227841
75.5%
57145
 
18.9%
1 16837
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 227841
75.5%
57145
 
18.9%
1 16837
 
5.6%

NPCHP35E
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
224888 
57145 
1
 
19790

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 224888
74.5%
57145
 
18.9%
1 19790
 
6.6%

Length

2024-05-06T23:47:44.818233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:44.927322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 224888
91.9%
1 19790
 
8.1%

Most occurring characters

ValueCountFrequency (%)
2 224888
74.5%
57145
 
18.9%
1 19790
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 224888
91.9%
1 19790
 
8.1%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 224888
74.5%
57145
 
18.9%
1 19790
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 224888
74.5%
57145
 
18.9%
1 19790
 
6.6%

NPCHP35F
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
235321 
57145 
1
 
9357

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 235321
78.0%
57145
 
18.9%
1 9357
 
3.1%

Length

2024-05-06T23:47:45.039393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:45.138768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 235321
96.2%
1 9357
 
3.8%

Most occurring characters

ValueCountFrequency (%)
2 235321
78.0%
57145
 
18.9%
1 9357
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 235321
96.2%
1 9357
 
3.8%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 235321
78.0%
57145
 
18.9%
1 9357
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 235321
78.0%
57145
 
18.9%
1 9357
 
3.1%

NPCHP35J
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
234122 
57145 
1
 
10556

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 234122
77.6%
57145
 
18.9%
1 10556
 
3.5%

Length

2024-05-06T23:47:45.251194image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:45.357014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 234122
95.7%
1 10556
 
4.3%

Most occurring characters

ValueCountFrequency (%)
2 234122
77.6%
57145
 
18.9%
1 10556
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 234122
95.7%
1 10556
 
4.3%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 234122
77.6%
57145
 
18.9%
1 10556
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 234122
77.6%
57145
 
18.9%
1 10556
 
3.5%

NPCHP35I
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2
227306 
57145 
1
 
17372

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters301823
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 227306
75.3%
57145
 
18.9%
1 17372
 
5.8%

Length

2024-05-06T23:47:45.471506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T23:47:45.576736image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2 227306
92.9%
1 17372
 
7.1%

Most occurring characters

ValueCountFrequency (%)
2 227306
75.3%
57145
 
18.9%
1 17372
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 244678
81.1%
Space Separator 57145
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 227306
92.9%
1 17372
 
7.1%
Space Separator
ValueCountFrequency (%)
57145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 301823
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 227306
75.3%
57145
 
18.9%
1 17372
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 301823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 227306
75.3%
57145
 
18.9%
1 17372
 
5.8%

FEX_C
Text

Distinct33342
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
2024-05-06T23:47:45.785726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.091077
Min length1

Characters and Unicode

Total characters3347542
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique568 ?
Unique (%)0.2%

Sample

1st row16,604442041
2nd row16,604442041
3rd row16,604442041
4th row26,046357048
5th row26,046357048
ValueCountFrequency (%)
1 21917
 
7.3%
4,1739956106 1336
 
0.4%
5,8873957278 1125
 
0.4%
3,3593691726 741
 
0.2%
12,432040251 719
 
0.2%
17,68478686 680
 
0.2%
9,2536882129 673
 
0.2%
30,327762873 598
 
0.2%
24,278983997 594
 
0.2%
1,9622544989 540
 
0.2%
Other values (33332) 272900
90.4%
2024-05-06T23:47:46.152768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 358842
10.7%
2 334050
10.0%
3 323249
9.7%
4 313102
9.4%
5 307393
9.2%
6 303038
9.1%
7 296945
8.9%
8 290768
8.7%
9 289373
8.6%
, 279906
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3067636
91.6%
Other Punctuation 279906
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 358842
11.7%
2 334050
10.9%
3 323249
10.5%
4 313102
10.2%
5 307393
10.0%
6 303038
9.9%
7 296945
9.7%
8 290768
9.5%
9 289373
9.4%
0 250876
8.2%
Other Punctuation
ValueCountFrequency (%)
, 279906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3347542
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 358842
10.7%
2 334050
10.0%
3 323249
9.7%
4 313102
9.4%
5 307393
9.2%
6 303038
9.1%
7 296945
8.9%
8 290768
8.7%
9 289373
8.6%
, 279906
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3347542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 358842
10.7%
2 334050
10.0%
3 323249
9.7%
4 313102
9.4%
5 307393
9.2%
6 303038
9.1%
7 296945
8.9%
8 290768
8.7%
9 289373
8.6%
, 279906
8.4%

Interactions

2024-05-06T23:46:59.106907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:32.648821image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:36.529921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.388435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:40.198438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:42.235284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:44.065806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.874481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.713476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.725294image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.527175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:53.296115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:55.115808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.915104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:59.229232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:32.780622image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:34.841522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.665559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.513031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:40.319967image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:42.361758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:44.187793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.998806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.832681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:51.653569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:53.424209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:55.240800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:57.045936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:59.346201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:32.892950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:34.952889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.782019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.628650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:40.436618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:42.480160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:44.309528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:46.119653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.945603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.962276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.767795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:59.462753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:36.901888image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.747835image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:40.555057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:44.426653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:48.056534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:40.881848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:42.729915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:44.552536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:33.262606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:35.326634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:37.160724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.005242image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:41.016613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:35.446761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:33.497283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:39.244388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:33.619067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:35.680178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:37.556562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.364220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:47:00.196724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:35.793943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:43.332754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.142206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:46.988665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.009095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:54.404193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.210407image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.019778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:47:00.322729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:33.861633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:35.912173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:37.796639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.603268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:41.619667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:43.454612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.261157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.110837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T23:46:50.925705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:52.709749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:54.526009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.326689image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.139657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:47:00.441119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:33.992587image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.033683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:37.911721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.717627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:41.736003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:43.576547image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.375749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.231565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.244254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.040528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:52.824236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:54.639588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.445299image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.262456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:47:00.561907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:34.128204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.164441image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.029958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.836960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:41.858180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:43.703869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.494789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.351908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.364207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.163837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:52.942913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:54.758909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.560656image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.380716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:47:00.682588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:34.456879image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.285918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.146924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:39.957621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:41.985816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:43.828171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.617405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.472770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.487848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.287798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:53.063341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:54.879020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.677605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.853208image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:47:00.801064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:34.594099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:36.408734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:38.271450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:40.079437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:42.113909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:43.948760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:45.750341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:47.594558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:49.609471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:51.409663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:53.181184image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:54.998166image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:56.797672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T23:46:58.984594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-06T23:47:02.204349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T23:47:05.710484image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ĆÆĀ»ĀæDIRECTORIO_PERDIRECTORIO_HOGDIRECTORIOSECUENCIA_PORDENNPCHP1NPCHP2NPCHP3NPCHP4NPCHP4ANPCHP5NPCHP6NPCHP6ANPCHP7NPCHP9ANPCHP9BNPCHP9CNPCHP9DNPCHP9ENPCHP9FNPCHP10NPCHP10ANPCHP11NPCHP11ANPCHP12NPCHP12ANPCHP13NPCHP13ANPCHP13BNPCHP14NPCHP15ANPCHP15BNPCHP16NPCHP17NPCHP18ANPCHP18BNPCHP18CNPCHP18DNPCHP18ENPCHP18FNPCHP18GNPCHP18HNPCHP18INPCHP18JNPCHP18KNPCHP18LNPCHP18MNPCHP19BNPCHP20NPCHP20ANPCHP20BNPCHP21ANPCHP21AANPCHP21BNPCHP21BANPCHP21CNPCHP21CANPCHP22NPCHP22ANPCHP23NPCHP23ANPCHP24NPCHP24_1ANPCHP24AANPCHP24ABNPCHP24_1BNPCHP24BANPCHP24BBNPCHP25ANPCHP25BNPCHP25CNPCHP25DNPCHP25ENPCHP25FNPCHP25GNPCHP25HNPCHP25INPCHP28NPCHP28ANPCHP28BNPCHP29ANPCHP29BNPCHP29CNPCHP29DNPCHP29ENPCHP29FNPCHP29GNPCHP29HNPCHP29INPCHP30ANPCHP30BNPCHP30CNPCHP30DNPCHP30ENPCHP30FNPCHP30GNPCHP30HNPCHP30INPCHP30JNPCHP30KNPCHP30LNPCHP31AANPCHP31ABNPCHP31BANPCHP31BBNPCHP31CANPCHP31CBNPCHP31DANPCHP31DBNPCHP31EANPCHP31EBNPCHP31FANPCHP31FBNPCHP32NPCHP32ANPCHP32BNPCHP33NPCHP34NPCHP34ANPCHP35ANPCHP35BNPCHP35CNPCHP35DNPCHP35ENPCHP35FNPCHP35JNPCHP35IFEX_C
01010001110100011010001112957111115452301233522222222216,604442041
1101000121010001101000121211112111110210103016001002212222216,604442041
2101000131010001101000131228551111111111158563520003042222222216,604442041
3101001111010011101001111192722112117000000222222116365301264126321212222226,046357048
41010011210100111010011212744111211301200002222222226,046357048
510100113101001110100113117211140221200000211000002115400000312114547241230100302022222122226,046357048
6101002111010021101002111251111101000451688002222222213,840826089
7101002121010021101002121266221114842301600002222222213,840826089
81010031110100311010031112351121130125511222222227,0111108805
910100312101003110100312125111111135140011222222227,0111108805
ĆÆĀ»ĀæDIRECTORIO_PERDIRECTORIO_HOGDIRECTORIOSECUENCIA_PORDENNPCHP1NPCHP2NPCHP3NPCHP4NPCHP4ANPCHP5NPCHP6NPCHP6ANPCHP7NPCHP9ANPCHP9BNPCHP9CNPCHP9DNPCHP9ENPCHP9FNPCHP10NPCHP10ANPCHP11NPCHP11ANPCHP12NPCHP12ANPCHP13NPCHP13ANPCHP13BNPCHP14NPCHP15ANPCHP15BNPCHP16NPCHP17NPCHP18ANPCHP18BNPCHP18CNPCHP18DNPCHP18ENPCHP18FNPCHP18GNPCHP18HNPCHP18INPCHP18JNPCHP18KNPCHP18LNPCHP18MNPCHP19BNPCHP20NPCHP20ANPCHP20BNPCHP21ANPCHP21AANPCHP21BNPCHP21BANPCHP21CNPCHP21CANPCHP22NPCHP22ANPCHP23NPCHP23ANPCHP24NPCHP24_1ANPCHP24AANPCHP24ABNPCHP24_1BNPCHP24BANPCHP24BBNPCHP25ANPCHP25BNPCHP25CNPCHP25DNPCHP25ENPCHP25FNPCHP25GNPCHP25HNPCHP25INPCHP28NPCHP28ANPCHP28BNPCHP29ANPCHP29BNPCHP29CNPCHP29DNPCHP29ENPCHP29FNPCHP29GNPCHP29HNPCHP29INPCHP30ANPCHP30BNPCHP30CNPCHP30DNPCHP30ENPCHP30FNPCHP30GNPCHP30HNPCHP30INPCHP30JNPCHP30KNPCHP30LNPCHP31AANPCHP31ABNPCHP31BANPCHP31BBNPCHP31CANPCHP31CBNPCHP31DANPCHP31DBNPCHP31EANPCHP31EBNPCHP31FANPCHP31FBNPCHP32NPCHP32ANPCHP32BNPCHP33NPCHP34NPCHP34ANPCHP35ANPCHP35BNPCHP35CNPCHP35DNPCHP35ENPCHP35FNPCHP35JNPCHP35IFEX_C
30181331783513317835131783513117441160211200001800002180000222111080040206206104222222221
301814317835143178351317835141172211602112000016000021800002221108004020004064211222221
3018153178441131784413178441112331630034160000222222221
3018163178441231784413178441212341750036170000222222221
30181731785911317859131785911123511040048160000222222221
301818317874113178741317874111233511060040160000222222221
301819317881113178811317881111229661620034160032222222221
3018203178811231788113178811212341840034160000222222221
3018213178851131788513178851112351840034120000222222221
3018223178851231788513178851212351860036170000222222221